### *<IP_ADDRESS>.1 MI visible channel calibration process*

As shown in Fig. 14, the MI visible channel calibration process was simply verification of a linear visible calibration equation using the real data sets. After that, necessity of the normalization among eight detectors was checked. Albedo monitor data analysis and moon image processing were used for the detector's trend monitoring.

Fig. 14. MI visible channel radiometric calibration process flow chart

The pixel-to-pixel response non-uniformity (PRNU) were examined using the both space look and image data (Fig. 15). PRNU met the requirement specifications (denoted by red

#### Fig. 15. PRNU Check (SIDE1): Space-Look data

250 Remote Sensing – Advanced Techniques and Platforms

COMS IOT (In-Orbit Test) MI calibration activities were divided into two main parts: MI visible channel and infrared channel calibrations. The visible channel calibration was conducted from July 11, 2010 after the COMS Launch (2010.6.26. 21:41 UTC). Calibration activity of the Infrared channels including the visible one was started from Aug 11, 2010 after the completion of the out-gassing (removal of remnant volatile contaminants by heating). The functional and performance tests were performed for both two functional sides (SIDE1: primary, SIDE2: secondary) plus two patch temperatures (patch Low and Mid) of the MI payload. In addition to the images of MI channels, albedo monitor and moon images were also acquired and analyzed. The final performance verification was checked officially at the phase 1 & phase 5 end meeting (Jan 26, 2011) after the intensive MI radiometric calibration processes conducted from July, 2010 to Jan, 2011. Summary of the

5. The performance tests of MI infrared channels based on the payload real-time

As shown in Fig. 14, the MI visible channel calibration process was simply verification of a linear visible calibration equation using the real data sets. After that, necessity of the normalization among eight detectors was checked. Albedo monitor data analysis and moon

The pixel-to-pixel response non-uniformity (PRNU) were examined using the both space look and image data (Fig. 15). PRNU met the requirement specifications (denoted by red

1. Command and control tests for both sides (Side 1/Side2) were successful

**<IP_ADDRESS> MI in-orbit radiometric calibration**

verifications at the meeting is listed as follows.

3. Image monitoring and acquisition tests were successful 4. The performance tests of MI visible channel were successful

operational configuration modes were successful

image processing were used for the detector's trend monitoring.

Fig. 14. MI visible channel radiometric calibration process flow chart

2. Scan mechanism tests were successful

*<IP_ADDRESS>.1 MI visible channel calibration process*

lines). As a result, the normalization algorithm was not implemented on the visible channel calibration process.

### *<IP_ADDRESS>.2 MI infrared channel radiometric calibration*

Different from the visible channel, the MI infrared channel calibration process has complex steps to get qualified data as shown in Fig. 16. First, coefficients of the basic (nominal) IR calibration equation were verified using the real data sets and then four major steps were taken: 1) Scan mirror emissivity compensation, 2) Midnight effect correction, 3) Slope averaging and 4) 1/f noise compensation.

Fig. 16. MI infrared channel radiometric calibration process flow chart

#### **1. Scan mirror emissivity correction**

Based on the scan mirror emissivity (as a function of a scan angle), the effect of emitted radiances from the coating material on the scan mirror were compensated. The computed scan mirror emissivities according to different scan angles are shown in Fig. 17.

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 253

The PRNU values from the radiometric indices computed from the real time MI data processing system of COMS (called IMPS) indicated that relative bias between detectors of infrared channels were minimal and thus the normalization process step on the infrared channels were skipped as same as the visible one. The complete COMS MI images resulted from the IOT (see Fig. 19) showed that the radiometric performance of the MI payload meets the all requirement specifications for the current operation configuration of MI (SIDE 1,

Fig. 19. Calibrated MI FD Level 1A images (before INR), (Side 1, Patch Low, 2010.12.23)

The GOCI was turned on for the first time in orbit on July 12, 2010 and captured it first image the day after. Both sides (primary and redundant) were successfully tested during about two weeks. After the successful functional tests such as the mechanism movement, detector temperature control, and imaging chain validity, the radiometric performance tests and radiometric calibration tests have been performed. The radiometric performance test is aimed to verify the validity of performance measured on ground. In-orbit offset and dark signal shows a quite good correlation with the ground measurements. Also the radiometric gain matrix, which has been measured in-orbit, is very similar to the ground gain. The SNR test results, which are provided in Table 5, show the performance exceeding the requirements in all 8 spectral bands by 25 to 40%. This is mainly due to the excellent quality of the CMOS matrix detector, and the design margin considered for worst case analysis.

GOCI in-orbit radiometric calibration relies on a full pupil Sun Diffuser (SD), made of fused silica, known to be insensitive to radiations. The instrument is designed to allow a calibration every day. In practice, during IOT, two calibrations per week were performed. After IOT, the frequency of calibration was reduced to one per week. The

**3.2.2 GOCI radiometric performances**

**<IP_ADDRESS> GOCI in-orbit radiometric calibration**

**<IP_ADDRESS> GOCI in-orbit SNR**

*<IP_ADDRESS>.3 The result of the MI IOT radiometric calibration processes*

Patch Low).

Fig. 17. Computation of the scan mirror emissivity for four different infrared chennels (1Dark Image, Side1, Patch Low, Det A, 2010.8.16).

### **2. Midnight effect compensation**

Before and after four hours of near local midnight data were corrected using a mid night compensation algorithm (see Fig. 18). The estimated slope(open circles and squares) based on the regression between the black body slope and the selected optic temperature were used near midnight and the original slope values(thick lines) are used during the rest of time.

Fig. 18. IR Midnight Effect: the result of slope selection (SWIR; Side 1/Patch Low)

#### **3. Slope averaging**

Slope averaging is a smoothing process to remove the responsivity variation of the detectors due to the diurnal variation of background radiation inside the sensor. The reference slope value were compared to that of the previous day and the residual between two were filtered by the slope averaging.

#### **4. 1/f noise compensation**

The 1/f noise compensation, which is a filtering of random noise on the lower frequency components was also conducted. After the 1/f noise compensation, the stripping effects on the water vapor channel were greatly removed.

Fig. 17. Computation of the scan mirror emissivity for four different infrared chennels

Fig. 18. IR Midnight Effect: the result of slope selection (SWIR; Side 1/Patch Low)

Slope averaging is a smoothing process to remove the responsivity variation of the detectors due to the diurnal variation of background radiation inside the sensor. The reference slope value were compared to that of the previous day and the residual between two were filtered

The 1/f noise compensation, which is a filtering of random noise on the lower frequency components was also conducted. After the 1/f noise compensation, the stripping effects on

Before and after four hours of near local midnight data were corrected using a mid night compensation algorithm (see Fig. 18). The estimated slope(open circles and squares) based on the regression between the black body slope and the selected optic temperature were used near midnight and the original slope values(thick lines) are used during the rest of time.

(1Dark Image, Side1, Patch Low, Det A, 2010.8.16).

**2. Midnight effect compensation**

**3. Slope averaging**

by the slope averaging. **4. 1/f noise compensation**

the water vapor channel were greatly removed.

## *<IP_ADDRESS>.3 The result of the MI IOT radiometric calibration processes*

The PRNU values from the radiometric indices computed from the real time MI data processing system of COMS (called IMPS) indicated that relative bias between detectors of infrared channels were minimal and thus the normalization process step on the infrared channels were skipped as same as the visible one. The complete COMS MI images resulted from the IOT (see Fig. 19) showed that the radiometric performance of the MI payload meets the all requirement specifications for the current operation configuration of MI (SIDE 1, Patch Low).

Fig. 19. Calibrated MI FD Level 1A images (before INR), (Side 1, Patch Low, 2010.12.23)

## **3.2.2 GOCI radiometric performances**

## **<IP_ADDRESS> GOCI in-orbit SNR**

The GOCI was turned on for the first time in orbit on July 12, 2010 and captured it first image the day after. Both sides (primary and redundant) were successfully tested during about two weeks. After the successful functional tests such as the mechanism movement, detector temperature control, and imaging chain validity, the radiometric performance tests and radiometric calibration tests have been performed. The radiometric performance test is aimed to verify the validity of performance measured on ground. In-orbit offset and dark signal shows a quite good correlation with the ground measurements. Also the radiometric gain matrix, which has been measured in-orbit, is very similar to the ground gain. The SNR test results, which are provided in Table 5, show the performance exceeding the requirements in all 8 spectral bands by 25 to 40%. This is mainly due to the excellent quality of the CMOS matrix detector, and the design margin considered for worst case analysis.

## **<IP_ADDRESS> GOCI in-orbit radiometric calibration**

GOCI in-orbit radiometric calibration relies on a full pupil Sun Diffuser (SD), made of fused silica, known to be insensitive to radiations. The instrument is designed to allow a calibration every day. In practice, during IOT, two calibrations per week were performed. After IOT, the frequency of calibration was reduced to one per week. The

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 255

1% amplitude. This is probably not the real variation of SD. In addition the longitudinal solar incident angle to the GOCI shows the similar variation over the year. The reason for this sinusoidal variation is now under examination. The variations lower than 1% over almost one year shows the SD stability. All the variations observed in orbit up to now are within 1 to 2% which is very low and very satisfactory. Some evolutions seem to be correlated with the longitudinal solar incident angle. This opens the way to further

The major performances (Modulation Transfer Function – MTF and Signal to Noise Ratio – SNR) are presented in this chapter, all other performances being well within the

One of the major advantages of ocean observation with the GOCI is that continuous monitoring is possible with images provided every hour, which maximizes chance of clear observation of the whole field even in cloudy season. No sun glint occurs thanks to the angular position of the field of view during daytime, while it discards many observations in

During IOT, the MI Ground Sampling Distance (GSD) and the spatial performance (MTF) have been fully checked and verified. The GSD has been verified as follows. The landmark matching results by the INRSM were used and the angular steps in both E/W and N/S were measured by best fit between level 1A image coordinates and landmark GEOS positions. Those angular steps were used to determine a projection function for each image (or subimage). Then, the specified GSD at Nadir was verified using the relevant projection

During IOT, the GOCI GSD has been verified by the same method as with MI, and the imaging coverage and the slot overlap have also been fully verified. The spatial performance (MTF) has also been checked. Before launch, the GOCI MTF performance was tested through ground test at the payload level. The in-orbit MTF test would allow the validation of MTF at system level including the satellite stability performance. But the measurement accuracy for in-orbit test is much worse than the ground test depending on the availability and the quality of the transition patterns between bright and dark in the image. The GOCI

improvement of the calibration model if necessary.

**3.3 Spatial and geometric performances of MI and GOCI**

Table 6 shows the measured MI MTF results.

Table 6. Measured In-Orbit MI MTF

requirements.

low orbit.

**<IP_ADDRESS> MI**

function.

**<IP_ADDRESS> GOCI**

**3.3.1 GSD and MTF**

Table 5. GOCI In-Orbit SNR test result

potential aging of the SD is monitored by a second diffuser (Diffuser Aging Monitoring Device: DAMD) used less frequency than the SD, typically once per month since the end of the IOT. When not in used, both SD and DAMD are well protected by the shutter wheel cover to minimise their exposure to the space environment.

Through IOT period, about six months, the instrument calibration and the calibration stability were fully verified. The purpose of radiometric calibration test is to verify the inorbit calibration method which is based on two point measurements (Kang & Coste, 2010). The in-orbit radiometric gain matrix of GOCI is calculated by using two sun images, which are obtained through the SD with two different integration times. The imaging time for the sun has been specified according to the desired solar incident angle over 25 degree to 35 degree. The actual solar incident angle of measured sun image is calculated by using the On-Board Time (OBT) which is included in the secondary header of the raw data. During IOT, sun imaging for eight spectral bands has been performed over two days based on one week period. For each calibration, six sets of sun images with short and long integration time have been obtained for each spectral band over about 10 minutes. Variation of gains calculated by 6 sets are very small (0.1 % to 0.3%) and are most probably due to processing noise (small errors in the ephemerides and in the calibration time) and also possibly to short term variations of the sun irradiance. Fig. 8 shows the gain evolution over eight months. For first three months, the gain shows a relatively rapid decrement. There is about 2% variation over eight months. Fig. 20 shows the aging factor of the SD over eight months. The trend provided in this Figure shows a sinusoidal variation over 8 months with about maximum

Fig. 20. In-orbit radiometric stability over 8 months

1% amplitude. This is probably not the real variation of SD. In addition the longitudinal solar incident angle to the GOCI shows the similar variation over the year. The reason for this sinusoidal variation is now under examination. The variations lower than 1% over almost one year shows the SD stability. All the variations observed in orbit up to now are within 1 to 2% which is very low and very satisfactory. Some evolutions seem to be correlated with the longitudinal solar incident angle. This opens the way to further improvement of the calibration model if necessary.

The major performances (Modulation Transfer Function – MTF and Signal to Noise Ratio – SNR) are presented in this chapter, all other performances being well within the requirements.

One of the major advantages of ocean observation with the GOCI is that continuous monitoring is possible with images provided every hour, which maximizes chance of clear observation of the whole field even in cloudy season. No sun glint occurs thanks to the angular position of the field of view during daytime, while it discards many observations in low orbit.

## **3.3 Spatial and geometric performances of MI and GOCI**

## **3.3.1 GSD and MTF**

### **<IP_ADDRESS> MI**

254 Remote Sensing – Advanced Techniques and Platforms

Band Mean SNR at GOCI level B1 1476 1077 B2 1496 1199 B3 1716 1316 B4 1722 1223 B5 1586 1192 B6 1513 1093 B7 1449 1107 B8 1390 1009

potential aging of the SD is monitored by a second diffuser (Diffuser Aging Monitoring Device: DAMD) used less frequency than the SD, typically once per month since the end of the IOT. When not in used, both SD and DAMD are well protected by the shutter wheel

Through IOT period, about six months, the instrument calibration and the calibration stability were fully verified. The purpose of radiometric calibration test is to verify the inorbit calibration method which is based on two point measurements (Kang & Coste, 2010). The in-orbit radiometric gain matrix of GOCI is calculated by using two sun images, which are obtained through the SD with two different integration times. The imaging time for the sun has been specified according to the desired solar incident angle over 25 degree to 35 degree. The actual solar incident angle of measured sun image is calculated by using the On-Board Time (OBT) which is included in the secondary header of the raw data. During IOT, sun imaging for eight spectral bands has been performed over two days based on one week period. For each calibration, six sets of sun images with short and long integration time have been obtained for each spectral band over about 10 minutes. Variation of gains calculated by 6 sets are very small (0.1 % to 0.3%) and are most probably due to processing noise (small errors in the ephemerides and in the calibration time) and also possibly to short term variations of the sun irradiance. Fig. 8 shows the gain evolution over eight months. For first three months, the gain shows a relatively rapid decrement. There is about 2% variation over eight months. Fig. 20 shows the aging factor of the SD over eight months. The trend provided in this Figure shows a sinusoidal variation over 8 months with about maximum

(a) Gain Stability (b) SD Stability

Fig. 20. In-orbit radiometric stability over 8 months

Table 5. GOCI In-Orbit SNR test result

cover to minimise their exposure to the space environment.

SNR specification

During IOT, the MI Ground Sampling Distance (GSD) and the spatial performance (MTF) have been fully checked and verified. The GSD has been verified as follows. The landmark matching results by the INRSM were used and the angular steps in both E/W and N/S were measured by best fit between level 1A image coordinates and landmark GEOS positions. Those angular steps were used to determine a projection function for each image (or subimage). Then, the specified GSD at Nadir was verified using the relevant projection function.

Table 6 shows the measured MI MTF results.

Table 6. Measured In-Orbit MI MTF

## **<IP_ADDRESS> GOCI**

During IOT, the GOCI GSD has been verified by the same method as with MI, and the imaging coverage and the slot overlap have also been fully verified. The spatial performance (MTF) has also been checked. Before launch, the GOCI MTF performance was tested through ground test at the payload level. The in-orbit MTF test would allow the validation of MTF at system level including the satellite stability performance. But the measurement accuracy for in-orbit test is much worse than the ground test depending on the availability and the quality of the transition patterns between bright and dark in the image. The GOCI

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 257

Worth noting is the fact that the COMS AOCS pointing performaces, as described in section 3.1, provide a significant contribution to the final INR performances. Also worth noting is the timeliness requirement put on the MI INR processing. As mentioned in section 2.2, the satellite serves as telecommunication relay to broadcast corrected data to end users in international formats called HRIT and LRIT. Both formats suppose to rectify the data both radiometrically and geometrically. An allowance of 15 minutes is given to perform the ground processing before uploading again the data to the satellite. After few inevitable tunings, the whole process is now performed in typically 12 minutes. For illustration purpose, two examples of shoreline matching are presented for MI vis channel and for one

Fig. 22. GOCI shoreline matching. The reference shoreline is superimposed to the

geometrically rectified GOCI image. Matching is better than 2 pixels over the whole area.

GOCI spectral band in the Fig. 21 and Fig. 22.

Fig. 21. MI shoreline matching (FD, VIS)

MTF is calculated by using the image having a radiometric transition (such as a coast line) which is equivalent to Knife Edge Function (KEF) measurement. Table 7 shows the GOCI MTF test result. Significant margins are demonstrated with respect to specifications; similar margins are present in all spectral bands.

Table 7. Measured GOCI MTF in the band 8

## **3.3.2 INR performances**

The INR IOT took a significant amount of time, as the final tuning requested. The first positive result obtained from the first images was the number of landmarks automatically extracted by the INR software. During the development, it had been demonstrated that a minimum of typically 100 landmarks were necessary, and sometimes more than 600 landmarks could be found on images.

The INR performance is evaluated on the basis on land marks residuals (statistical error after landmark best fit). In order to verify the validity of this approach, the coast line from the images is checked against an absolute coast line (based on GSHHS). The following figures in the Table 8 illustrate the typical performances of COMS INR as observed during the IOT.

Table 8. COMS MI and GOCI INR performances (units in µrad)

MTF is calculated by using the image having a radiometric transition (such as a coast line) which is equivalent to Knife Edge Function (KEF) measurement. Table 7 shows the GOCI MTF test result. Significant margins are demonstrated with respect to specifications; similar

> *Sample #1* 0.34 0.42 0.38 0.30 *Sample #2* 0.27 0.43 0.37 0.36 *Sample #3* 0.29 0.33 0.42 0.33 *Sample #4* 0.28 0.45 0.37 0.32 *Sample #5* 0.35 0.37 0.37 0.26 *Sample #6* 0.40 0.42 0.38 0.31

> > **0.30** *19%*

The INR IOT took a significant amount of time, as the final tuning requested. The first positive result obtained from the first images was the number of landmarks automatically extracted by the INR software. During the development, it had been demonstrated that a minimum of typically 100 landmarks were necessary, and sometimes more than 600

The INR performance is evaluated on the basis on land marks residuals (statistical error after landmark best fit). In order to verify the validity of this approach, the coast line from the images is checked against an absolute coast line (based on GSHHS). The following figures in the Table 8 illustrate the typical performances of COMS INR as observed during

*Sample #7* 0.31 0.44 *Sample #8* 0.43 0.34 *Sample #9* 0.32 0.28 *Sample #10* 0.32 0.36

Table 8. COMS MI and GOCI INR performances (units in µrad)

**EWN S**

*0.36 0.35 0.06 0.04*

> **0.30** *16%*

**Band 8**

margins are present in all spectral bands.

*MTF @ Nyquist*

*Mean Value Standard Error Specification (Mean - Spec.) / Spec.*

landmarks could be found on images.

**3.3.2 INR performances**

the IOT.

Table 7. Measured GOCI MTF in the band 8

Worth noting is the fact that the COMS AOCS pointing performaces, as described in section 3.1, provide a significant contribution to the final INR performances. Also worth noting is the timeliness requirement put on the MI INR processing. As mentioned in section 2.2, the satellite serves as telecommunication relay to broadcast corrected data to end users in international formats called HRIT and LRIT. Both formats suppose to rectify the data both radiometrically and geometrically. An allowance of 15 minutes is given to perform the ground processing before uploading again the data to the satellite. After few inevitable tunings, the whole process is now performed in typically 12 minutes. For illustration purpose, two examples of shoreline matching are presented for MI vis channel and for one GOCI spectral band in the Fig. 21 and Fig. 22.

Fig. 21. MI shoreline matching (FD, VIS)

Fig. 22. GOCI shoreline matching. The reference shoreline is superimposed to the geometrically rectified GOCI image. Matching is better than 2 pixels over the whole area.

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 259

As mentioned in the previous sections, COMS MI Level 1B data are generated through radiometric and geometric calibrations and then sixteen meteorological products(level 2) are produced by CMDPS (COMS Meteorological Data Processing System) as shown Fig. 25.

Parts of meteorological products from COMS MI have been generated operationally since April 1, 2011 together with COMS operation. Those products are cloud analysis (type, phase and amount), cloud top temperature/pressure, atmospheric motion vector, cloud detection, fog, and aerosol index. And then, four products, which are sea surface temperature, rain

(a) (b) (c)

Fig. 26. Examples of COMS meteorological products (a) cloud phase (b) atmospheric

**4.1 MI**

**4.1.1 Generation of MI end products**

Fig. 25. COMS Meteorological Products

meteorological vector and (c) rain intensity.

Further analysis and monitoring on INR performances have been performed since the start of normal operation of COMS for the service to the end users, and Fig. 23 and Fig. 24 illustrate some of these typical COMS INR performances.

Mode: ENH, Channel: VIS and IR, and negative correlation between the number of LMKs and the average of Residuals (courtesy of KMA)

Mode: ENH, Channel: VIS and IR, and negative correlation between the number of LMKs and the average of Residuals: Twilight effects (courtesy of KMA)

Fig. 24. MI INR performance during 1st April ~ 31th August.

## **4. Application and suggestion**

It has been merely 8 months since the outset of the normal operation of COMS for the distribution and service of the images and image products to the end users and scientific communities. The activities in this period in terms of the data processing, calibration and the end product generation and the related studies and researches have been exceedingly interesting, proactive and imaginative, to say the least, and in a word 'dynamic' in a very positive and rewarding sense. This section describes the application aspect of the COMS image data from MI and GOCI, addresses some posing technical challenges at the present time on this course of data application, summarizes some of the representative end products both from MI and GOCI and discusses the way forwards with some suggestions.

## **4.1 MI**

258 Remote Sensing – Advanced Techniques and Platforms

Further analysis and monitoring on INR performances have been performed since the start of normal operation of COMS for the service to the end users, and Fig. 23 and Fig. 24

Mode: ENH, Channel: VIS and IR, and negative correlation between the number of LMKs and the

Mode: ENH, Channel: VIS and IR, and negative correlation between the number of LMKs and the

It has been merely 8 months since the outset of the normal operation of COMS for the distribution and service of the images and image products to the end users and scientific communities. The activities in this period in terms of the data processing, calibration and the end product generation and the related studies and researches have been exceedingly interesting, proactive and imaginative, to say the least, and in a word 'dynamic' in a very positive and rewarding sense. This section describes the application aspect of the COMS image data from MI and GOCI, addresses some posing technical challenges at the present time on this course of data application, summarizes some of the representative end products

both from MI and GOCI and discusses the way forwards with some suggestions.

illustrate some of these typical COMS INR performances.

Fig. 23. MI INR performance during 1st April ~ 31th August.

average of Residuals: Twilight effects (courtesy of KMA)

**4. Application and suggestion**

Fig. 24. MI INR performance during 1st April ~ 31th August.

average of Residuals (courtesy of KMA)

## **4.1.1 Generation of MI end products**

As mentioned in the previous sections, COMS MI Level 1B data are generated through radiometric and geometric calibrations and then sixteen meteorological products(level 2) are produced by CMDPS (COMS Meteorological Data Processing System) as shown Fig. 25.

Fig. 25. COMS Meteorological Products

Parts of meteorological products from COMS MI have been generated operationally since April 1, 2011 together with COMS operation. Those products are cloud analysis (type, phase and amount), cloud top temperature/pressure, atmospheric motion vector, cloud detection, fog, and aerosol index. And then, four products, which are sea surface temperature, rain

Fig. 26. Examples of COMS meteorological products (a) cloud phase (b) atmospheric meteorological vector and (c) rain intensity.

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 261

phase and the disappearance phase, based on the MI infrared (IR) images, and automatically analyses the Typhoon intensity through the experience in pattern recognition by applying the Fast Fourier Transform (FFT) on the resulting patterns from the different phase of the

COMS MI data are also to be used in the generation of aeoronautical meteorological products, as shown in the Fig. 28. These products may have a relatively low accuracy but have the advantage of observing the broader area every hour. They are providing the level 2 information; such as the cloud phase, cloud height and the cloud top temperature in the air route and also the information from the convective cloud monitoring, and the other technique is under development for the information generation on the elements that can

(a) (b)

The application of GOCI data is focused on the monitoring of long-term/short-term ocean change phenomena around Korean peninsula and north-eastern Asian seas. In daytime, the hourly-produced GOCI data will be used for the ocean/coast environmental monitoring and for the observation of ocean dynamics features and the management of ocean territory. Also, these GOCI data, when used in conjunction with ocean numeric models, would bring

GOCI level 2 data products can be generated from GOCI level 1B with GDPS (GOCI Data Processing System) which is the data processing and analysis software developed by

This GDPS system derives the pure ocean signal (water leaving radiance) by atmospheric correction using aero-optics model and oceano-optics model developed and modified by KORDI. It can extract pure water signal as the normalized water leaving radiance which is corrected water leaving radiance by considering the satellite - sun relative geometry. For geostationary satellite, this relative position of the sun and the satellite changes all the time

Fig. 28. Examples of COMS MI aeoronautical meteorological products (under

developments) (a) turbulence distribution (b) icing on airplane area .

forth the increase of accuracy in ocean forecasting,.

cause aircraft accidents, such as the icing and the turbulence.

cyclone.

**4.2 GOCI**

KORDI.

intensity, outgoing longwave radiation, and upper tropospheric humidity, were generated additionally from 10 August 2011. These products are currently being validated through comparison between satellite-derived products and ground in-situ data. For example, detection area of Asian dust (aerosol index) occurred in 2011 April and May was compared with COMS GOCI and MODIS (Moderate Resolution Imaging Spectroradiometer) true color images or OMI (Ozone Monitoring Instrument) AOD (Aerosol Optical Depth). The other six products which are land surface temperature, sea ice/snow cover, total precipitable water, insolation, clear sky radiance, and aerosol optical depth will be operationally produced soon.

## **4.1.2 Application to weather forecasting and analysis**

In Korean peninsula, annual losses and damages in human and material are enormous due to the convective cloud accompanying summer heavy rainfall, which is either flown from the West Sea or originated locally. COMS can monitor and watch the origination and development of this convective cloud since it can observe Korean peninsula with MI in a concentrative way eight times an hour. NMSC is supporting the weather forecasting with the developed technique for Very Short Range Forecasting utilizing COMS MI meteorological data, which was introduced and derived from the technique of convective cloud rainfall intensity calculation and monitoring by the SAFNWC (Satellite Application Facilities Nowcasting) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites).

Fig. 27. Examples of COMS MI data applications (a) Convective rain intensity image combined with radar rain map (b) Predicted location of convective cloud and lightening image.

To analyze the Typhoon, which passes through Korean peninsula two to three times a year, typically around July to September time, such elements as the Typhoon intensity, radius of strong winds, the maximum wind speed, low pressure, are needed. In this analysis, NMSC is utilizing the Advanced Dvorak Technique (ADT) in the site operations, which was developed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) of University of Wisconsin (UW). The algorithm in this technique classifies the evolution phase of the tropical cyclone according to its intensity, as the formation phase, the development phase and the disappearance phase, based on the MI infrared (IR) images, and automatically analyses the Typhoon intensity through the experience in pattern recognition by applying the Fast Fourier Transform (FFT) on the resulting patterns from the different phase of the cyclone.

COMS MI data are also to be used in the generation of aeoronautical meteorological products, as shown in the Fig. 28. These products may have a relatively low accuracy but have the advantage of observing the broader area every hour. They are providing the level 2 information; such as the cloud phase, cloud height and the cloud top temperature in the air route and also the information from the convective cloud monitoring, and the other technique is under development for the information generation on the elements that can cause aircraft accidents, such as the icing and the turbulence.

Fig. 28. Examples of COMS MI aeoronautical meteorological products (under developments) (a) turbulence distribution (b) icing on airplane area .

## **4.2 GOCI**

260 Remote Sensing – Advanced Techniques and Platforms

intensity, outgoing longwave radiation, and upper tropospheric humidity, were generated additionally from 10 August 2011. These products are currently being validated through comparison between satellite-derived products and ground in-situ data. For example, detection area of Asian dust (aerosol index) occurred in 2011 April and May was compared with COMS GOCI and MODIS (Moderate Resolution Imaging Spectroradiometer) true color images or OMI (Ozone Monitoring Instrument) AOD (Aerosol Optical Depth). The other six products which are land surface temperature, sea ice/snow cover, total precipitable water, insolation, clear sky radiance, and aerosol optical depth will be operationally produced

In Korean peninsula, annual losses and damages in human and material are enormous due to the convective cloud accompanying summer heavy rainfall, which is either flown from the West Sea or originated locally. COMS can monitor and watch the origination and development of this convective cloud since it can observe Korean peninsula with MI in a concentrative way eight times an hour. NMSC is supporting the weather forecasting with the developed technique for Very Short Range Forecasting utilizing COMS MI meteorological data, which was introduced and derived from the technique of convective cloud rainfall intensity calculation and monitoring by the SAFNWC (Satellite Application Facilities Nowcasting) of EUMETSAT (European Organization for the Exploitation of

(a) (b)

To analyze the Typhoon, which passes through Korean peninsula two to three times a year, typically around July to September time, such elements as the Typhoon intensity, radius of strong winds, the maximum wind speed, low pressure, are needed. In this analysis, NMSC is utilizing the Advanced Dvorak Technique (ADT) in the site operations, which was developed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) of University of Wisconsin (UW). The algorithm in this technique classifies the evolution phase of the tropical cyclone according to its intensity, as the formation phase, the development

Fig. 27. Examples of COMS MI data applications (a) Convective rain intensity image combined with radar rain map (b) Predicted location of convective cloud and lightening

**4.1.2 Application to weather forecasting and analysis**

soon.

image.

Meteorological Satellites).

The application of GOCI data is focused on the monitoring of long-term/short-term ocean change phenomena around Korean peninsula and north-eastern Asian seas. In daytime, the hourly-produced GOCI data will be used for the ocean/coast environmental monitoring and for the observation of ocean dynamics features and the management of ocean territory. Also, these GOCI data, when used in conjunction with ocean numeric models, would bring forth the increase of accuracy in ocean forecasting,.

GOCI level 2 data products can be generated from GOCI level 1B with GDPS (GOCI Data Processing System) which is the data processing and analysis software developed by KORDI.

This GDPS system derives the pure ocean signal (water leaving radiance) by atmospheric correction using aero-optics model and oceano-optics model developed and modified by KORDI. It can extract pure water signal as the normalized water leaving radiance which is corrected water leaving radiance by considering the satellite - sun relative geometry. For geostationary satellite, this relative position of the sun and the satellite changes all the time

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 263

Understanding of sea surface currents and estimation of pollutant movements

Coastal water quality control/monitoring

Long-term climate change monitoring

Coastal ocean eutrophication

Carbon cycle

**PRODUCTS DESCRIPTION APPLICATION**

Fig. 29. Examples of GOCI level 2 end products, TSS (Total Suspended Sediment) and

GOCI products such as ocean current vector and ocean color properties would be provided to the fishery and the related organization for the increase of the haul, the effective management of fish, and finally the increase of fisheries income. The GOCI data could also be useful for monitoring suspended sediment movement, pollution particles movement, ocean current circulation and ocean ecosystem. Also, it will contribute to the international cooperation system, such as GEOSS (Global Earth Observation System of Systems), for the long-term ocean climate change related research and application by the data exchange and

The Korea Ocean Satellite Center (KOSC) in KORDI as the official GOCI operation agency, receives the GOCI data from the satellite directly, generates, stores, manages and distributes the processed standard products. And KOSC will continuously develop new ocean environmental analysis algorithms to apply to the imagery data of GOCI and the GOCI-II

Through the normal operation of GOCI, KOSC can provide the new, high-grade ocean environmental information in near-real time. It can be applied to the detection of freak phenomena of ocean nature such as the red-tide and the green-tide. The primary

Sea surface current direction/speed

compounds from

estimation

Coastal water quality level

The production of Organic

carbon dioxide, principally through the process of photosynthesis

**Sea surface**

**Primary**

**Water quality Level (WQL)**

**Productivity (PP)**

Table 10. GOCI level 3 data products

CDOM (Colored Dissolved Organic Matter)

co-research among related countries.

which is next generation of GOCI.

**current vector (WCV)**

and then the ocean signal is distorted. To resolve this issue of signal distortion, some research was performed. The system can generate the marine environment analysis data using specific algorithms for target region. The data processing algorithms applied to the existing ocean satellite optical sensor and new algorithms to the GOCI would produce the latest marine environmental analysis results.

Table 9 shows the list of GOCI level 2 data products which are currently being generated and used for each application purpose, and Table 10 signifies the list of GOCI level 3 data products which can also be generated by GDPS. The algorithm to generate GOCI level 3 data is under the final validation process. Fig. 29 shows some typical examples of these end products, in the case of TSS and CDOM.

Table 9. GOCI level 2 data products

Table 10. GOCI level 3 data products

and then the ocean signal is distorted. To resolve this issue of signal distortion, some research was performed. The system can generate the marine environment analysis data using specific algorithms for target region. The data processing algorithms applied to the existing ocean satellite optical sensor and new algorithms to the GOCI would produce the

Table 9 shows the list of GOCI level 2 data products which are currently being generated and used for each application purpose, and Table 10 signifies the list of GOCI level 3 data products which can also be generated by GDPS. The algorithm to generate GOCI level 3 data is under the final validation process. Fig. 29 shows some typical examples of these end

**PRODUCTS DESCRIPTION APPLICATION**

**Red tide (RI)** Red tide index information Ocean pollution and ecological

**PRODUCTS DESCRIPTION APPLICATION**

Indispensible for water color

Ocean primary production estimation, dumping site monitoring, climate change

Coastal ocean environmental analysis and monitoring TSS movement and transfer monitoring

Indicator of ocean pollution Ocean salinity estimation

Ocean optical properties analysis

Movement and transfer monitoring

Navy tactics, ocean pollution map,

Input data for the water analysis

analysis algorithms

algorithm

monitoring

monitoring

of red tide

application

Yellow dust, Vegetation Index Atmospheric environment and land

sea rescue work

Climate change trend analysis

Fishing ground detection Fishing ground environmental

information

The radiance assumed to be measured at the very surface of the water under

The water leaving radiance assumed to be measured at nadir, as if there was no atmosphere with the Sun at

Concentration of phytoplankton chlorophyll in ocean water

concentration in ocean water

concentration in ocean water

Degree of clarity of the ocean observed by the naked eye

> Daily 8 images composite for cloud free mosaic image

Fishing ground probability

index, fishing ground prediction

Absorption coefficient(a) Backscattering coefficient(bb)

latest marine environmental analysis results.

products, in the case of TSS and CDOM.

the atmosphere

zenith

**TSS** Total suspended sediment

**CDOM** Colored dissolved organic matter

K-coefficient

**Water-leaving Radiance (Lw)**

**Normalized water**

**Chlorophyll (CHL)**

**(nLw)**

**Optical properties of water**

**Underwater Visibility (VIS)**

**Atm. & earth environment**

**Daily composite of CHL, SS, CDOM**

**Fishing ground Information (FGI)**

Table 9. GOCI level 2 data products

**leaving radiance**

Fig. 29. Examples of GOCI level 2 end products, TSS (Total Suspended Sediment) and CDOM (Colored Dissolved Organic Matter)

GOCI products such as ocean current vector and ocean color properties would be provided to the fishery and the related organization for the increase of the haul, the effective management of fish, and finally the increase of fisheries income. The GOCI data could also be useful for monitoring suspended sediment movement, pollution particles movement, ocean current circulation and ocean ecosystem. Also, it will contribute to the international cooperation system, such as GEOSS (Global Earth Observation System of Systems), for the long-term ocean climate change related research and application by the data exchange and co-research among related countries.

The Korea Ocean Satellite Center (KOSC) in KORDI as the official GOCI operation agency, receives the GOCI data from the satellite directly, generates, stores, manages and distributes the processed standard products. And KOSC will continuously develop new ocean environmental analysis algorithms to apply to the imagery data of GOCI and the GOCI-II which is next generation of GOCI.

Through the normal operation of GOCI, KOSC can provide the new, high-grade ocean environmental information in near-real time. It can be applied to the detection of freak phenomena of ocean nature such as the red-tide and the green-tide. The primary

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 265

Oil spill monitoring - Oil spill and monitoring of movement and distribution of pollution

matter dissolved in seawater

contaminant migration path

productivity improvement

Hurricane watch - A hurricane tracking and navigation path

monitoring information

atmospheric environment

long-term climate change

information

utilization

Table 11. Application subjects of GOCI data

management of fisheries resources

Sea-ice monitoring - Development of the area of sea ice observations and monitoring

Forest fire monitoring - land management and forest fire monitoring, forest resources

Dust monitoring - Dust, vegetation, and the atmosphere and global environmental

Current surveillance - Balm of seawater, and the flow rate information production - Utilize coastal water quality management

Turbidity Monitoring - Indicators of marine pollution

Fishery Information - Fish and Fishery distribution

monitoring

monitoring)

phytoplankton concentration and monitoring the amount of organic

of red tide-related damage contribute to the reduction

productivity of aquatic organisms in the environment

Operation Application Items

Red Tide Monitoring

Green Algae Monitoring

Speculative waters, environmental monitoring

Low-salinity water monitoring

Fisheries and fishfarm management

El Niño, La Niña monitoring

tidal

Ecological monitoring

productivity derived from the GOCI chlorophyll and other products is the key research information about ocean carbon circulation. The color RGB images and analysis images of GOCI products with high spatial resolution are clearer and more recognizable than the monochrome images from other existing geostationary earth monitoring satellite which has only 1 visible band. These images can be useful to land application and atmospheric remote sensing application like monitoring of typhoon, sea ice, forest fire, yellow dust, etc.

Fig. 30. Standard RGB image of GOCI (left) and the analysis result of seawater chlorophyll density in the East Sea (right)

Table 11 shows the overall scheme of GOCI data application, and Fig. 31 and Fig. 32 exemplify some of the typical applications. In Fig. 32, several Images of different dates were mosaiced to realize this cloud-free picture and the numerical signals of the Yellow Sea (East China Sea), the East Sea (Japan Sea) and Northwestern Pacific were differently processed to maintain a balanced tone throughout the whole coverage area of the GOCI.

productivity derived from the GOCI chlorophyll and other products is the key research information about ocean carbon circulation. The color RGB images and analysis images of GOCI products with high spatial resolution are clearer and more recognizable than the monochrome images from other existing geostationary earth monitoring satellite which has only 1 visible band. These images can be useful to land application and atmospheric remote

sensing application like monitoring of typhoon, sea ice, forest fire, yellow dust, etc.

Fig. 30. Standard RGB image of GOCI (left) and the analysis result of seawater chlorophyll

Table 11 shows the overall scheme of GOCI data application, and Fig. 31 and Fig. 32 exemplify some of the typical applications. In Fig. 32, several Images of different dates were mosaiced to realize this cloud-free picture and the numerical signals of the Yellow Sea (East China Sea), the East Sea (Japan Sea) and Northwestern Pacific were differently processed to

> - Analysis of marine primary productivity - Long-term climate change research in the ocean

and utilized to secure carbon credits

maintain a balanced tone throughout the whole coverage area of the GOCI.

density in the East Sea (right)

Operation Application Items

Carbon Circulation Monitoring

Table 11. Application subjects of GOCI data

COMS, the New Eyes in the Sky for Geostationary Remote Sensing 267

COMS is a unique bird in many ways, partially in that it is such a complex satellite accommodating three different payloads with rather conflicting missions into a single spacecraft bus, partially in that it employs a unique and novel INR system, and also partially because it has the GOCI on board, the world's 1st geostationary imager for the ocean colour. By the joint effort of EADS Astrium and KARI, it was masterfully designed, developed, tested, and launched, and is now behaving beautifully in orbit, exhibiting quite impressive and fruitful performances along with the very useful and interesting image data and

It is especially interesting to note that with the co-existence of both MI and GOCI on board, the comparison and combination of data taken by these two sensors from the same geostationary location, could open some new windows for further interesting research and development. In the case of GOCI, the benefits of Geo observation compared to its LEO (Low Earth Orbit) counterparts has been notably demonstrated and largely appreciated by the end users so far, even with the relatively short accumulated time of normal service, and as the further activities on post processing and related studies will get refined and matured,

With these observations, findings and expectations at hand, it could be cautiously said that COMS image data and the processed end products will bring some added dimension to the world remote sensing community and the related field of science and technology. For this end, it will be made sure that the application of MI and GOCI data during the mission life of COMS is to be fully exploited and maximized. It is hoped and believed that all the aspects of the COMS development and operation; from the design, implementation, test and validation, launch and IOT, to the data processing, end product generation, data utilization and end user services will continue to grow and be improved and expanded in its relevant

COMS program has involved so many different organizations, agencies, government bureaus and companies and wide spectrum of participating personnel with different cultures, characters and backgrounds. It is with such a great emotion and gratitude, along with the highly rewarding feeling and sense of proud accomplishment, that we can now say that we regard all the participating members in this program as one big 'COMS family' and to some, very close life-long friends, indeed. There were certainly some bumpy roads and rocky times in the course, but through them all we became real friends and it is grateful that we can now look back upon those days with sense of mutual respect and appreciation.

We feel deeply grateful and obliged to send our appreciation to our Korean government bureaus first and foremost; MEST (Ministry of Education, Science and Technology), KMA (Korea Meteorological Administration), KORDI (Korea Ocean Research & Development Institute), MLTM (Ministry of Land, Transport and Maritime Affairs) and MIC (Ministry of Information and Communication), among others, without whose support and dedication this grand program would not have been possible. We feel especially thankful to MOSF (Ministry Of Strategy and Finance) for providing us the actual revenue sources continually

it is expected that this trend will become even more prominent.

realm into the next generation of geostationary remote sensing satellites.

throughout the entire course of this challenging program.

**5. Conclusion**

processed end products.

**6. Acknowledgment**

Fig. 31. Land and Sea Features expressed by natural color on the full scene of the GOCI.

Fig. 32. Structure of Chlorophyll Distribution in the North-East Asian Seas.

## **5. Conclusion**

266 Remote Sensing – Advanced Techniques and Platforms

Fig. 31. Land and Sea Features expressed by natural color on the full scene of the GOCI.

Fig. 32. Structure of Chlorophyll Distribution in the North-East Asian Seas.

COMS is a unique bird in many ways, partially in that it is such a complex satellite accommodating three different payloads with rather conflicting missions into a single spacecraft bus, partially in that it employs a unique and novel INR system, and also partially because it has the GOCI on board, the world's 1st geostationary imager for the ocean colour. By the joint effort of EADS Astrium and KARI, it was masterfully designed, developed, tested, and launched, and is now behaving beautifully in orbit, exhibiting quite impressive and fruitful performances along with the very useful and interesting image data and processed end products.

It is especially interesting to note that with the co-existence of both MI and GOCI on board, the comparison and combination of data taken by these two sensors from the same geostationary location, could open some new windows for further interesting research and development. In the case of GOCI, the benefits of Geo observation compared to its LEO (Low Earth Orbit) counterparts has been notably demonstrated and largely appreciated by the end users so far, even with the relatively short accumulated time of normal service, and as the further activities on post processing and related studies will get refined and matured, it is expected that this trend will become even more prominent.

With these observations, findings and expectations at hand, it could be cautiously said that COMS image data and the processed end products will bring some added dimension to the world remote sensing community and the related field of science and technology. For this end, it will be made sure that the application of MI and GOCI data during the mission life of COMS is to be fully exploited and maximized. It is hoped and believed that all the aspects of the COMS development and operation; from the design, implementation, test and validation, launch and IOT, to the data processing, end product generation, data utilization and end user services will continue to grow and be improved and expanded in its relevant realm into the next generation of geostationary remote sensing satellites.

## **6. Acknowledgment**

COMS program has involved so many different organizations, agencies, government bureaus and companies and wide spectrum of participating personnel with different cultures, characters and backgrounds. It is with such a great emotion and gratitude, along with the highly rewarding feeling and sense of proud accomplishment, that we can now say that we regard all the participating members in this program as one big 'COMS family' and to some, very close life-long friends, indeed. There were certainly some bumpy roads and rocky times in the course, but through them all we became real friends and it is grateful that we can now look back upon those days with sense of mutual respect and appreciation.

We feel deeply grateful and obliged to send our appreciation to our Korean government bureaus first and foremost; MEST (Ministry of Education, Science and Technology), KMA (Korea Meteorological Administration), KORDI (Korea Ocean Research & Development Institute), MLTM (Ministry of Land, Transport and Maritime Affairs) and MIC (Ministry of Information and Communication), among others, without whose support and dedication this grand program would not have been possible. We feel especially thankful to MOSF (Ministry Of Strategy and Finance) for providing us the actual revenue sources continually throughout the entire course of this challenging program.

**12**

*USA*

**Hyperspectral Remote Sensing –**

**Small Vessels in Coastal Littoral Areas**

Large field of view sensors as well as flight line tracks of hyperspectral reflectance signatures are useful for helping to help solve many land and water environmental management problems and issues. High spectral and spatial resolution sensing systems are useful for environmental monitoring and surveillance applications of land and water features, such as species discrimination, bottom top identification, and vegetative stress or vegetation dysfunction assessments1. In order to help provide information for environmental quality or environmental security issues, it is safe to say that there will never be one set of sensing systems to address all problems. Thus an optimal set of sensors and platforms need to be considered and then selected. The purpose of this paper is to describe a set of sensing systems that have been integrated and can be useful for land and water related assessments related to monitoring after an oil spill (specifically for weathered oil) and related recovery efforts. Recently collected selected imagery and data are presented from flights that utilize an aircraft with a suite of sensors and cameras. Platform integration, modifications and sensor mounting was achieved using designated engineering representatives (DER) analyses, and related FAA field approvals in order to satisfy safety

Sensors utilized have been: (1) a photogrammetric 9 inch mapping camera utilizing a 12 inch focal length cone, and using AGFA X400PE1 color negative film that has been optimized for high resolution scanning (2400 dpi) in order to reduce the effects of newton rings and an associated special glass plate from Scanatronics in the Netherlands; (2) forward and aft full high definition (HD) video cameras recording to solid state memory with GPS encoding; (3) a forward mounted Nikon SLR 12.3 megapixel digital camera with a vibration reduction zoom lens and GPS encoding; (4) a high hyperspectral imaging system with 1376 spatial

**1. Introduction**

needs and requirements.

pixels and 64 to 1040 spectral bands.

**2.1 Imaging systems, sensor systems and calibration**

**2. Techniques**

Charles R. Bostater, Jr., Gaelle Coppin and Florian Levaux *Marine Environmental Optics Laboratory and Remote Sensing Center, College of Engineering, Florida Institute of Technology, Melbourne, Florida*

**Using Low Flying Aircraft and**

The author and co-authors of this chapter only represent a very small portion of all COMS family members, and we believe that the authors of this chapter, in fact, ought to be all COMS family members and thus we feel deeply indebted to them. Our special thanks go to; Mr. Seong-rae Jung and Ms. Jin Woo of KMA, and Mr. Hee-jeong Han and Mr. Seong-ik Cho of KORDI, for the charts in the section 3.3.2 and for their great help and support in preparing and finalizing sections 4.1 and 4.2.

Last but clearly not the least, we remember our missing COMS family members, Mr. Daniel Buvat of EADS Astrium and Mr. Young-joon Chang and Mr. Sang-mu Moon of KARI, who abruptly departed from this life on earth in the course of COMS development and operation, leaving the rest of us in deep grief and helpless devastation. Along the lines of COMS history, with the trace of their sincere commitment and contribution to the success of COMS, they will always be remembered in our hearts. We dedicate this small chapter to them.

### **7. References**

## **Hyperspectral Remote Sensing – Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas**

Charles R. Bostater, Jr., Gaelle Coppin and Florian Levaux *Marine Environmental Optics Laboratory and Remote Sensing Center, College of Engineering, Florida Institute of Technology, Melbourne, Florida USA*

## **1. Introduction**

268 Remote Sensing – Advanced Techniques and Platforms

The author and co-authors of this chapter only represent a very small portion of all COMS family members, and we believe that the authors of this chapter, in fact, ought to be all COMS family members and thus we feel deeply indebted to them. Our special thanks go to; Mr. Seong-rae Jung and Ms. Jin Woo of KMA, and Mr. Hee-jeong Han and Mr. Seong-ik Cho of KORDI, for the charts in the section 3.3.2 and for their great help and support in

Last but clearly not the least, we remember our missing COMS family members, Mr. Daniel Buvat of EADS Astrium and Mr. Young-joon Chang and Mr. Sang-mu Moon of KARI, who abruptly departed from this life on earth in the course of COMS development and operation, leaving the rest of us in deep grief and helpless devastation. Along the lines of COMS history, with the trace of their sincere commitment and contribution to the success of COMS, they will always be remembered in our hearts. We dedicate this small chapter to

Cros, G.; Loubières, P.; Lainé, I.; Ferrand, S.; Buret, T.; Guay, P. (June 2011) *European ASTRIX*

Kang, G.; Coste, P. (2010) *An In-orbit Radiometric Calibration Method of the Geostationary Ocean*

Kim, H.; Kang, G.; Ellis, B.; Nam, M.; Youn, H.; Faure, F.; Coste, P.; Servin, P. (2009)

Kim, H.; Meyer, P.; Crombez, V.; Harris, J. (2010) *COMS INR: Prospect and Retrospect,* 61st

Lambert, H.; Koeck, C.; Kim, H.; Degremont, J.; Laine, I. (2011) *One Year into the Success of the COMS Mission,* 62nd International Astronautical Congress (IAC 2011) KARI (Korea Aerospace Research Institute) (January 2006). *COMS Ground Segment*

Lee, B.; Jeong, W.; Lee, S.; et al. (April 2006). *Funtional Design of COMS Satellite Ground*

Lim, H.; Ahn, S.; Seo, S.; Park, D. (December 2011). *In-Orbit Test Operational Validation of the*

*Control System*, Conference of the Korean soceiety for aeronautical and space

*COMS Image Data Acquisition and Control System*, Journal of the Korean soceiety of

Object Identifier 10.1109 / TGRS. 2010. 2050329

International Astronautical Congress (IAC 2010)

*Specification, Ref C1-SP-800-001-Rev.C*, Deajeon, KOREA

Astronautical Congress (IAC 2009)

sceience, pp. 1000-1005, KSAS06-1850.

Space Tehnology, Vol.6 No.2, pp. 1- 9.

*FOGS In-Orbit Heritage*, 8th International ESA Conference on Guidance, Navigation

*Color Imager (GOCI)*, IEEE Transactions on Geoscience and Remote Sensing, Digital

*Geostationary Ocean Color Imager (GOCI)*, *Overview and Prospect,* 60th International

preparing and finalizing sections 4.1 and 4.2.

& Control Systems

them.

**7. References**

Large field of view sensors as well as flight line tracks of hyperspectral reflectance signatures are useful for helping to help solve many land and water environmental management problems and issues. High spectral and spatial resolution sensing systems are useful for environmental monitoring and surveillance applications of land and water features, such as species discrimination, bottom top identification, and vegetative stress or vegetation dysfunction assessments1. In order to help provide information for environmental quality or environmental security issues, it is safe to say that there will never be one set of sensing systems to address all problems. Thus an optimal set of sensors and platforms need to be considered and then selected. The purpose of this paper is to describe a set of sensing systems that have been integrated and can be useful for land and water related assessments related to monitoring after an oil spill (specifically for weathered oil) and related recovery efforts. Recently collected selected imagery and data are presented from flights that utilize an aircraft with a suite of sensors and cameras. Platform integration, modifications and sensor mounting was achieved using designated engineering representatives (DER) analyses, and related FAA field approvals in order to satisfy safety needs and requirements.

## **2. Techniques**

#### **2.1 Imaging systems, sensor systems and calibration**

Sensors utilized have been: (1) a photogrammetric 9 inch mapping camera utilizing a 12 inch focal length cone, and using AGFA X400PE1 color negative film that has been optimized for high resolution scanning (2400 dpi) in order to reduce the effects of newton rings and an associated special glass plate from Scanatronics in the Netherlands; (2) forward and aft full high definition (HD) video cameras recording to solid state memory with GPS encoding; (3) a forward mounted Nikon SLR 12.3 megapixel digital camera with a vibration reduction zoom lens and GPS encoding; (4) a high hyperspectral imaging system with 1376 spatial pixels and 64 to 1040 spectral bands.

Hyperspectral Remote Sensing –

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 271

Fig. 1. Image (upper left) of a scanned AGFA color negative film (X400PE1) from an airborne flight March 21, 2011 over Barataria Bay, LA. The upper right image is a simultaneously collected hyperspectral RGB image (540, 532, 524 nm). Imagery indicates the ability to detect weathered oil in the area from oil spill remediation activities. The graph shows selected

Fig. 2. Digital images of the *weathered oil* observed in early 2011 from the ground in the

Barataria Bay, LA. areas shown above.

spectra in weathered oil impact areas. The lower right shows in-situ targets.

The HSI imaging system utilizes a pen tablet computer with custom software. The HSI pushbroom system is integrated into the computer with an external PCMCIA controller card for operating the temperature stabilized monochrome camera which is bore sighted with a transmission spectrograph and ~39 degree field of view lens. The HSI imaging system is gimbal mounted and co-located with one of the HD 30 HZ cameras. The HSI system runs between ~20 to 90 HZ and is also co-located with a ~100 HZ inertial measurement unit (IMU). The IMU is strap down mounted to the HSI along the axis of view of the hyperspectral imager.

An additional 5HZ WAAS GPS output is recorded as another data stream into the custom software that allows on the fly changes to the integration time and spectral binning capability of the system. The HSI system is calibrated for radiance using calibration spheres and with spectral line sources for wavelength calibration. Flights are conducted with the 5 cameras in a fashion to allow simultaneous and or continuous operation with additional use of camera intervalometers that trigger the Nikon and photogrammetric camera. Examples of imagery taken on March 21, 2011 are shown below as well as spectral signatures and in-situ field targets that are typically utilized for processing imagery for subsurface or submerged water feature detection and enhancements.

Airborne imagery shown in this paper was collected at 1,225 m between 10 AM local time or 4 PM local, with a 1/225 second shutter speed and aperture adjusted for optimal contrast and exposure. The large format (9 in2) negatives scanned at 2400 dpi using a scanner and a special glass plate obtained from Scanatron, Netherlands allows for minimization of "newton rings" in the resulting ~255 megapixel multispectral imagery shown below (left image). Experience has shown that this method works well with AGFA X400PE1 film. The aerial negative scanning process is calibrated using a scanned target with known submillimeter scales 0.005 mm to 5 um resolution using a 2400 dpi scanner. The film scanning process results in three band multispectral images with spectral response curves published by the film manufacturer (Agfa or Kodak).

*In-sit*u targets as shown in Figure 1 are used for calibration of the imagery using a combination of white, black or gray scale targets as shown below in an airborne digital image (right). Airborne targets are used for calibrating traditional film and digital sensor data for spatial and spectral characteristics using *in-situ* floating targets in the water as shown below.

Targets (figure 2) are placed along flight lines. These types of land and water targets are used for image enhancement techniques, for use as GPS georeferencing ground control points, and georeferencing accuracy assessments. They are necessary in order to assess shoreline erosion estimation resulting from oil spill impacts along littoral zones.

Figure 2 below shows images of weather oil taken in the Jimmy Bay area in January, 2011, eight months after the major spill was contained in the deep waters of the northern Gulf of Mexico.

## **2.2 Pushbroom imagery corrections for aerial platform motions**

Airborne pushbroom imagery collected aboard moving platforms (ground, air, sea, space) requires geometric corrections due to platform motions. These motions are due to changes in the linear direction of the platform (flight direction changes), as well as sensor and platform motion due to yaw, pitch and roll motions. Unlike frame cameras that acquire a 2 dimensional image, pushbroom cameras acquire one scan line at a time. A sequence of

The HSI imaging system utilizes a pen tablet computer with custom software. The HSI pushbroom system is integrated into the computer with an external PCMCIA controller card for operating the temperature stabilized monochrome camera which is bore sighted with a transmission spectrograph and ~39 degree field of view lens. The HSI imaging system is gimbal mounted and co-located with one of the HD 30 HZ cameras. The HSI system runs between ~20 to 90 HZ and is also co-located with a ~100 HZ inertial measurement unit (IMU). The IMU is strap down mounted to the HSI along the axis of view of the hyperspectral imager. An additional 5HZ WAAS GPS output is recorded as another data stream into the custom software that allows on the fly changes to the integration time and spectral binning capability of the system. The HSI system is calibrated for radiance using calibration spheres and with spectral line sources for wavelength calibration. Flights are conducted with the 5 cameras in a fashion to allow simultaneous and or continuous operation with additional use of camera intervalometers that trigger the Nikon and photogrammetric camera. Examples of imagery taken on March 21, 2011 are shown below as well as spectral signatures and in-situ field targets that are typically utilized for processing imagery for subsurface or submerged

Airborne imagery shown in this paper was collected at 1,225 m between 10 AM local time or 4 PM local, with a 1/225 second shutter speed and aperture adjusted for optimal contrast and exposure. The large format (9 in2) negatives scanned at 2400 dpi using a scanner and a special glass plate obtained from Scanatron, Netherlands allows for minimization of "newton rings" in the resulting ~255 megapixel multispectral imagery shown below (left image). Experience has shown that this method works well with AGFA X400PE1 film. The aerial negative scanning process is calibrated using a scanned target with known submillimeter scales 0.005 mm to 5 um resolution using a 2400 dpi scanner. The film scanning process results in three band multispectral images with spectral response curves published

*In-sit*u targets as shown in Figure 1 are used for calibration of the imagery using a combination of white, black or gray scale targets as shown below in an airborne digital image (right). Airborne targets are used for calibrating traditional film and digital sensor data for spatial and

Targets (figure 2) are placed along flight lines. These types of land and water targets are used for image enhancement techniques, for use as GPS georeferencing ground control points, and georeferencing accuracy assessments. They are necessary in order to assess

Figure 2 below shows images of weather oil taken in the Jimmy Bay area in January, 2011, eight months after the major spill was contained in the deep waters of the northern Gulf of

Airborne pushbroom imagery collected aboard moving platforms (ground, air, sea, space) requires geometric corrections due to platform motions. These motions are due to changes in the linear direction of the platform (flight direction changes), as well as sensor and platform motion due to yaw, pitch and roll motions. Unlike frame cameras that acquire a 2 dimensional image, pushbroom cameras acquire one scan line at a time. A sequence of

spectral characteristics using *in-situ* floating targets in the water as shown below.

shoreline erosion estimation resulting from oil spill impacts along littoral zones.

**2.2 Pushbroom imagery corrections for aerial platform motions**

water feature detection and enhancements.

by the film manufacturer (Agfa or Kodak).

Mexico.

Fig. 1. Image (upper left) of a scanned AGFA color negative film (X400PE1) from an airborne flight March 21, 2011 over Barataria Bay, LA. The upper right image is a simultaneously collected hyperspectral RGB image (540, 532, 524 nm). Imagery indicates the ability to detect weathered oil in the area from oil spill remediation activities. The graph shows selected spectra in weathered oil impact areas. The lower right shows in-situ targets.

Fig. 2. Digital images of the *weathered oil* observed in early 2011 from the ground in the Barataria Bay, LA. areas shown above.

Hyperspectral Remote Sensing –

*ref z*

(0,0,0)

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 273

obtain the relative position of each scan line and the corresponding spatial pixel shift that needs to be applied to correct the image. When a gimbal mounted HSI pushbroom camera is used, there are two main influences that cause the geometric distortions. These are the slowly varying directional changes of the platform and the roll induced motions. The first step in the algorithm is to use the GPS to calculate the position of the sensor (Ox,Oy,Oz) at every scan line. The second step accounts for the influence of the roll motion by using the IMU sensor data. The position of a pixel on the earth's surface can be estimated using:

*<sup>s</sup> x Ox h Oz s s y Oy h Oz s*

*<sup>x</sup> DEM z y*

( )

( )

(1)

*ref x*

hDEM

*DEM*

*z*

Where ( Sx, Sy, Sz ) are components of a unit central scan line ray vector, (x, y, z) the position in meters compared to the origin (the initial position of the center of the scan line) and hDEM

roll

*<sup>b</sup> <sup>b</sup> <sup>x</sup> <sup>y</sup>*

pitch

*bz*

(, ,) *x y z*

Fig. 4. This figure shows the position of the sensor (Ox, Oy, Oz) and the unit scan line ray vector in the reference coordinate system as well as the body (sensor platform) coordinate system with the possible platform motions. The position (x, y, z) is the position of the surface in the reference coordinate system that is located at the center of a HSI pixel .

*ref y*

*z h*

is the surface elevation given in meters with respect to Mean Sea Level (MSL).

(, ,) *<sup>x</sup> y z s s s*

yaw

(,,) *Ox Oy Oz*

*DEM*

scan lines acquired along the platform track allows the creation of a 2 dimensional image. The consequence of using this type of imaging system is that the scan lines collected produce spatial artifacts due to platform motion changes - resulting in scan line feature offsets. The following describes the roll induced problem to be corrected. Consider an airplane that is flying over a straight road indicated by the dark red, vertical line in the left image below. Now assume the airplane or mobile platform undergoes unwanted platform roll motion and thus the resulting straight feature in the acquired scene is curved, as suggested by the light, blue line in the left image. One knows that the road was straight so the image as shown in Figure 3 (right) indicates a lateral scan line adjustment is required in order to straighten the feature (the blue line). This is accomplished by "shifting" the scan lines opposite to the platform roll motion and results in an image where the feature in the image is corrected. Thus, one needs to calculate the offset that corresponds to the shift the pixels undergo.

Fig. 3. The left figure shown in blue is a distorted road. The red line corresponds to the center of the scan line. The right image represents the corrected version of the left image. On this image the blue straight line is the road and the red curve is the actual position of the center pixels of the scan lines. In this example only the shift in the cross track direction is represented.

The offset mentioned previously can be corrected if sensing geometry and the hyperspectral imaging (HSI) system orientations are known when the different scan lines were taken. To obtain the platform and sensor orientation changes and position a 60 Hz update rate inertial measurement unit (IMU) was utilized and mounted to the gimbal mounted camera. An IMU is a device that is comprised of triads of accelerometers and gyroscopes. The accelerometers measure specific forces along their axes which are accelerations due to gravity plus dynamic accelerations. The gyroscopes measure angular rates. The IMU (Motion Node, GLI Interactive LLC, Seattle, Washington) that is used also has 3 magnetometers and outputs the orientation immediately by using those 3 types of sensors. In addition, differential WAAS 5 HZ GPS position, directional deviations, altitude with respect to a specified datum, and platform speed are collected during the flights.

An adaptive Kalman filter is used to estimate the induced platform motions using the combined sensor data from the GPS and IMU. The filtering technique thus allows one to

scan lines acquired along the platform track allows the creation of a 2 dimensional image. The consequence of using this type of imaging system is that the scan lines collected produce spatial artifacts due to platform motion changes - resulting in scan line feature offsets. The following describes the roll induced problem to be corrected. Consider an airplane that is flying over a straight road indicated by the dark red, vertical line in the left image below. Now assume the airplane or mobile platform undergoes unwanted platform roll motion and thus the resulting straight feature in the acquired scene is curved, as suggested by the light, blue line in the left image. One knows that the road was straight so the image as shown in Figure 3 (right) indicates a lateral scan line adjustment is required in order to straighten the feature (the blue line). This is accomplished by "shifting" the scan lines opposite to the platform roll motion and results in an image where the feature in the image is corrected. Thus, one needs to calculate the offset that corresponds to the shift the

Fig. 3. The left figure shown in blue is a distorted road. The red line corresponds to the center of the scan line. The right image represents the corrected version of the left image. On this image the blue straight line is the road and the red curve is the actual position of the center pixels of the scan lines. In this example only the shift in the cross track direction is

The offset mentioned previously can be corrected if sensing geometry and the hyperspectral imaging (HSI) system orientations are known when the different scan lines were taken. To obtain the platform and sensor orientation changes and position a 60 Hz update rate inertial measurement unit (IMU) was utilized and mounted to the gimbal mounted camera. An IMU is a device that is comprised of triads of accelerometers and gyroscopes. The accelerometers measure specific forces along their axes which are accelerations due to gravity plus dynamic accelerations. The gyroscopes measure angular rates. The IMU (Motion Node, GLI Interactive LLC, Seattle, Washington) that is used also has 3 magnetometers and outputs the orientation immediately by using those 3 types of sensors. In addition, differential WAAS 5 HZ GPS position, directional deviations, altitude with respect to a specified datum, and

An adaptive Kalman filter is used to estimate the induced platform motions using the combined sensor data from the GPS and IMU. The filtering technique thus allows one to

pixels undergo.

represented.

platform speed are collected during the flights.

obtain the relative position of each scan line and the corresponding spatial pixel shift that needs to be applied to correct the image. When a gimbal mounted HSI pushbroom camera is used, there are two main influences that cause the geometric distortions. These are the slowly varying directional changes of the platform and the roll induced motions. The first step in the algorithm is to use the GPS to calculate the position of the sensor (Ox,Oy,Oz) at every scan line. The second step accounts for the influence of the roll motion by using the IMU sensor data. The position of a pixel on the earth's surface can be estimated using:

$$\begin{aligned} \mathbf{x} &= \mathbf{O}\mathbf{x} + \frac{\mathbf{s}\_x}{\mathbf{s}\_x} (\mathbf{h}\_{\text{DEM}} - \mathbf{O}\mathbf{z})\\ \mathbf{y} &= \mathbf{O}\mathbf{y} + \frac{\mathbf{s}\_y}{\mathbf{s}\_z} (\mathbf{h}\_{\text{DEM}} - \mathbf{O}\mathbf{z})\\ \mathbf{z} &= \mathbf{h}\_{\text{DEM}} \end{aligned} \tag{1}$$

Where ( Sx, Sy, Sz ) are components of a unit central scan line ray vector, (x, y, z) the position in meters compared to the origin (the initial position of the center of the scan line) and hDEM is the surface elevation given in meters with respect to Mean Sea Level (MSL).

Fig. 4. This figure shows the position of the sensor (Ox, Oy, Oz) and the unit scan line ray vector in the reference coordinate system as well as the body (sensor platform) coordinate system with the possible platform motions. The position (x, y, z) is the position of the surface in the reference coordinate system that is located at the center of a HSI pixel .

Hyperspectral Remote Sensing –

reference coordinate system.

matrix Rk (6x6 matrix).

indicates the time dependence.

given below by equation (4) for x, y and z.

∆tk = the time-interval (in seconds) between step k and k+1.

exactly compensated by the thrust and gravity by the lift.

second) in the reference coordinate system obtained by the GPS.

assumed white and Gaussian with covariance matrix Qk (6x6 matrix).

relates the state vector to the measurement vector (**z**k = Hk**x**k).

velocity. **z**k is thus always prone to measurement noise.

matrices are defined as follows in the reference coordinate frame:

*t*

*t*

*k k*

*A Q*

Detailed calculations of the covariance's ߪ௩

and horizontal position (in m2) given by the GPS.

*t*

where:

where:

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 275

**x**k = the state vector (6x1 matrix). **x**k = (Ox Oy Oz Vx Vy Vz)kT contains the position of the sensor (Ox,Oy,Oz) (in meters) and the velocity (Vx,Vy,Vz) (in meters per second) in the

Ak = the (6x6) matrix that gives the relation between the previous state vector to the current state vector when no noise and no input vector are considered . This relation can also be

Bk = the (6xm) matrix that relates the optional input vector u to the current state. (m = the

**u**k = the control input vector (mx1 matrix), we assume that there are no external forces that act upon the system so **u**k =0 in our application. Actually, it is assumed that the drag is

**z**k = the measurement vector (6x1 matrix). **z**k = = (Oxm Oym Ozm Vxm Vym Vzm)kT contains the position of the sensor (Oxm,Oym,Ozm) (in meters) and velocity (Vxm,Vym,Vzm) (in meters per

Hk = the measurement sensitivity (6x6) matrix also known as the observation matrix that

**w**k = the process noise or also called dynamic disturbance noise (6x1 matrix) which is

**v**k = the measurement noise of the GPS (6x1 matrix) which is also assumed white and Gaussian (detailed calculations of the covariances see below) and its associated covariance

The subscript k refers to the time step at which the vector or matrix is considered and

**x**k contains the real position and velocity, whereas **z**k contains the measured position and

In the first step, GPS data is used to calculate the position of the sensor (Ox,Oy,Oz) the

001 0 0 0 0 0.1 0 0 0 , 0 0 0 1 0 0 0 0 0 0.1 0 0

0 0 0 0 1 0 0 0 0 0 0.1 0 0 0 0 0 0 1 0 0 0 0 0 0.1

<sup>ଶ</sup> and ߪ

*k k*

<sup>ଶ</sup> are respectively the covariance in vertical

1 0 0 0 0 0.1 0 0 0 0 0 0 1 0 0 0 0 0.1 0 0 0 0

number of elements in the control input vector if external forces are considered).

x xx k1 k k O O V (4)

The reference coordinate system chosen in this paper is a local tangent plane with the x axis pointed in the initial along track direction, y axis is 90 degrees clockwise to the x axis and corresponds to the initial cross track direction. In the results that are presented in this paper, shifts have only be applied in the cross-track direction. The shifts in meters are scaled to shifts in pixels as a function of the altitude (given by the GPS in meters), the field of view of the sensor (dependent upon the lens used) and the number of pixels in one scan line.

In the following section a description of the system is given, as well as the assumptions made. Then the application of the Kalman filter to acquire the position and velocity of the sensor is described with a detailed description of the vectors and matrices used. In the 2nd paragraph of this section, the influence of roll is taken into account. Then a paragraph that describes the image resampling phase applied to low flying airborne imagery in littoral areas.

In general, the application of the Kalman filter is used to acquire the position and velocity of the sensor is described with a detailed description of the vectors and matrices used and influence of roll is taken into account as described below, followed by a nearest neighbor resampling of the HSI imagery for each band independently.

Results that are presented in this paper, pixel shifts are only applied in the cross-track direction. Use of a gimbal sensor mount has allowed reduced HSI sensor motion corrections, however the need for improving image corrections in order to include pitch and yaw motions have been developed.

Fig. 5. This figure shows the different altitudes and heights used. Where h is the altitude of the platform with respect to the WGS84, hDEM is the surface elevation, halt is the altitude of the platform with respect to MSL and hH is the true altitude with respect to the earth's surface. In our applications we consider that the surface elevation is negligible as we take images of oil spills around MSL, so hH ≈ halt.

#### **2.3 Description of the platform dynamic system**

In order to model the movement of the platform, a discrete dynamic system described by the canonical state space equations are used:

$$\mathbf{x}\_{k+1} = A\_k \mathbf{x}\_k + B\_k \mathbf{u}\_k + \mathbf{w}\_k \tag{2}$$

$$\mathbf{z}\_k = \mathbf{H}\_k \mathbf{x}\_k + \mathbf{v}\_k \tag{3}$$

where:

274 Remote Sensing – Advanced Techniques and Platforms

The reference coordinate system chosen in this paper is a local tangent plane with the x axis pointed in the initial along track direction, y axis is 90 degrees clockwise to the x axis and corresponds to the initial cross track direction. In the results that are presented in this paper, shifts have only be applied in the cross-track direction. The shifts in meters are scaled to shifts in pixels as a function of the altitude (given by the GPS in meters), the field of view of the sensor (dependent upon the lens used) and the number of pixels in one scan

In the following section a description of the system is given, as well as the assumptions made. Then the application of the Kalman filter to acquire the position and velocity of the sensor is described with a detailed description of the vectors and matrices used. In the 2nd paragraph of this section, the influence of roll is taken into account. Then a paragraph that describes the image resampling phase applied to low flying airborne imagery in littoral

In general, the application of the Kalman filter is used to acquire the position and velocity of the sensor is described with a detailed description of the vectors and matrices used and influence of roll is taken into account as described below, followed by a nearest neighbor

Results that are presented in this paper, pixel shifts are only applied in the cross-track direction. Use of a gimbal sensor mount has allowed reduced HSI sensor motion corrections, however the need for improving image corrections in order to include pitch and yaw

Fig. 5. This figure shows the different altitudes and heights used. Where h is the altitude of the platform with respect to the WGS84, hDEM is the surface elevation, halt is the altitude of the platform with respect to MSL and hH is the true altitude with respect to the earth's surface. In our applications we consider that the surface elevation is negligible as we take

In order to model the movement of the platform, a discrete dynamic system described by

**x x uw** *k kk k k k* <sup>1</sup> *A B* (2)

**z xv** *k kk k H* (3)

resampling of the HSI imagery for each band independently.

line.

areas.

motions have been developed.

images of oil spills around MSL, so hH ≈ halt.

the canonical state space equations are used:

**2.3 Description of the platform dynamic system**

**x**k = the state vector (6x1 matrix). **x**k = (Ox Oy Oz Vx Vy Vz)kT contains the position of the sensor (Ox,Oy,Oz) (in meters) and the velocity (Vx,Vy,Vz) (in meters per second) in the reference coordinate system.

Ak = the (6x6) matrix that gives the relation between the previous state vector to the current state vector when no noise and no input vector are considered . This relation can also be given below by equation (4) for x, y and z.

$$\left(\big|\mathcal{O}\_{\mathbf{x}}\right)\_{\mathbf{k}+1} = \left(\big|\mathcal{O}\_{\mathbf{x}}\right)\_{\mathbf{k}} + \left(\big|V\_{\mathbf{x}}\right)\_{\mathbf{k}}\tag{4}$$

where:

∆tk = the time-interval (in seconds) between step k and k+1.

Bk = the (6xm) matrix that relates the optional input vector u to the current state. (m = the number of elements in the control input vector if external forces are considered).

**u**k = the control input vector (mx1 matrix), we assume that there are no external forces that act upon the system so **u**k =0 in our application. Actually, it is assumed that the drag is exactly compensated by the thrust and gravity by the lift.

**z**k = the measurement vector (6x1 matrix). **z**k = = (Oxm Oym Ozm Vxm Vym Vzm)kT contains the position of the sensor (Oxm,Oym,Ozm) (in meters) and velocity (Vxm,Vym,Vzm) (in meters per second) in the reference coordinate system obtained by the GPS.

Hk = the measurement sensitivity (6x6) matrix also known as the observation matrix that relates the state vector to the measurement vector (**z**k = Hk**x**k).

**w**k = the process noise or also called dynamic disturbance noise (6x1 matrix) which is assumed white and Gaussian with covariance matrix Qk (6x6 matrix).

**v**k = the measurement noise of the GPS (6x1 matrix) which is also assumed white and Gaussian (detailed calculations of the covariances see below) and its associated covariance matrix Rk (6x6 matrix).

The subscript k refers to the time step at which the vector or matrix is considered and indicates the time dependence.

**x**k contains the real position and velocity, whereas **z**k contains the measured position and velocity. **z**k is thus always prone to measurement noise.

In the first step, GPS data is used to calculate the position of the sensor (Ox,Oy,Oz) the matrices are defined as follows in the reference coordinate frame:

Detailed calculations of the covariance's ߪ௩ <sup>ଶ</sup> and ߪ <sup>ଶ</sup> are respectively the covariance in vertical and horizontal position (in m2) given by the GPS.

Hyperspectral Remote Sensing –

corrected estimate of the state vector <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and ܲ

Kk (equation 6) is the Kalman gain (6x6 matrix).

estimated <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and ܲ

state vector (6x1 vector), ܲ

to estimate the state vector:

and non-smoothed state.

**4. Roll correction**

where: ˆ*s*

ܲ

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 277

 1 1

*P APA Q*

*k kk k*

*P I KH P*

<sup>௦</sup> = the covariance (6x6) matrix of the smoothed estimated state vector.

because the IMU data is given at a higher frequency than the GPS data.

*<sup>k</sup>* **x** = the smoothed estimated state vector (6x1 matrix).

*C PA P*

ି and ܲ

corrected value of the estimation covariance of the state vector, or:

ˆˆ ˆ *k k k kk k k*

*K H*

where, <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** are respectively the predicted (-) and corrected (+) value of the estimated

The Kalman filter thus computes a weighted average of the predicted and the measured state vector by using the Kalman gain Kk. If one has an accurate GPS sensor, the uncertainty on the measurement will be small so there will be more weight given to the measurement and thus the corrected estimate will be close to the measurement. When one has a non-accurate sensor, the uncertainty on the measurement is large and more weight will be given to the predicted estimate. A Kalman smoother has been applied as well where the equations are shown in (7) below. A Kalman Smoother in addition to the past observations also incorporates future observations

*T k kk k s s k k k k kk s sT k k kk k k*

ˆˆ ˆ ˆ

*P P CP P C*

Ck = the (6x6) matrix that determines the weight of the correction between the smoothed

The second step in the algorithm for motion correction accounts for the influence of the roll motion by using the IMU orientation output. This is not included in the first Kalman filter

1

*C A*

1 1

1 1

**xx x x** (7)

*k k k k kk*

*K PH HPH R*

*k kk k k*

and the measurement update step (given by equation 6 below) corrects the predicted

ା or:

<sup>222222</sup> ,,,,, *<sup>k</sup> Ox Oy Oz Vx Vy Vz <sup>k</sup> P diag*

11 1 1

ି using the additional GPS sensor measurements **z**k to obtain the

1

**xx z x** (6)

ା (a 6 x 6 matrix) are respectively the predicted and

**x x** (5)

ˆ ˆ *k kk*

*A*

$$H\_{k} = \begin{pmatrix} 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 \end{pmatrix}, \quad R\_{k} = \begin{pmatrix} \sigma\_{h}^{2} & 0 & 0 & 0 & 0 & 0 \\ 2 & 0 & 0 & 0 & 0 & 0 \\ 0 & \frac{\sigma\_{h}^{2}}{2} & 0 & 0 & 0 & 0 \\ 0 & 0 & \sigma\_{v}^{2} & 0 & 0 & 0 \\ 0 & 0 & 0 & \frac{\sigma\_{\tilde{V}h}^{2}}{2} & 0 & 0 \\ 0 & 0 & 0 & 0 & \frac{\sigma\_{\tilde{V}h}^{2}}{2} & 0 \\ 0 & 0 & 0 & 0 & 0 & \sigma\_{\tilde{V}\tilde{V}\_{k}}^{2} \end{pmatrix}, \quad \tilde{\sigma}\_{\tilde{V}\tilde{V}\_{k}} = \begin{pmatrix} \sigma\_{h}^{2} & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & \sigma\_{\tilde{V}\tilde{V}\_{k}}^{2} \end{pmatrix}$$

The covariance's of the vertical and horizontal velocities ��� � and ��� � (in m2 per seconds2) are however not given by the GPS but are calculated using using the following, where:

*A given quantity y is a function of x1, x2, … xN given by the formula y=f(x1,x2,...,xN). The uncertainties in xi are respectively e1, e2, …, eN. The absolute uncertainty ey is then given by*

$$\left(\left(e\_y\right)^2 = \left(\frac{\partial f}{\partial \mathbf{x}\_1}\right)^2 \left(e\_1\right)^2 + \left(\frac{\partial f}{\partial \mathbf{x}\_2}\right)^2 \left(e\_2\right)^2 + \dots + \left(\frac{\partial f}{\partial \mathbf{x}\_N}\right)^2 \left(e\_N\right)^2$$

From the above, one has for the vertical, z direction ���)� <sup>=</sup> ���)�������)� ��� and hence the covariance of the vertical velocity ���� � )� equals ��� �)������� �)� ��� since the velocity at time k equals the difference between the position at time k+1 and k, divided by the time interval. It is assumed that there is no uncertainty on the time interval. This is valid because one assumes that **x**k and **x**k+1 are statistically independent. In an similar manner, one can calculate the covariance of the horizontal velocity, where one assumes

 $\sigma\_{O\chi}^2 = \sigma\_{O\chi}^2 = \frac{\sigma\_h^2}{2}$  from  $\sigma\_h^2 = \sigma\_{O\chi}^2 + \sigma\_{O\chi}^2$ .

#### **3. Kalman filter and smoothing approach**

#### **3.1 Position and velocity estimations**

The Kalman filter consists of 2 steps. A temporal update step (also known as the "a priori" prediction step) and a measurement update step (also known as the "a posteriori" correction step). In the temporal step given by equations 4 and 5, the estimated state vector <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and the estimation covariance �� � at time step k are predicted based on the current knowledge at time step k-1.

The state vector ˆ *<sup>k</sup>* **x** contains the estimated position of the sensor (Ox,Oy,Oz) (in meters) and the velocity (Vx,Vy,Vz) (in meters per second) in the reference coordinate system.

The predictive procedure step is given by:

$$\begin{aligned} \hat{\mathbf{x}}\_{k}^{-} &= A\_{k-1} \hat{\mathbf{x}}\_{k-1}^{+} \\ P\_{k}^{-} &= A\_{k-1} P\_{k-1}^{+} A\_{k-1}^{\prime} + Q\_{k-1} \end{aligned} \tag{5}$$

and the measurement update step (given by equation 6 below) corrects the predicted estimated <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and ܲ ି using the additional GPS sensor measurements **z**k to obtain the corrected estimate of the state vector <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and ܲ ା or:

$$\begin{aligned} \hat{\mathbf{x}}\_{k} &= P\_{k}^{-} H\_{k}^{\prime} \left( H\_{k} P\_{k}^{-} H\_{k}^{\prime} + R\_{k} \right)^{-1} \\ \hat{\mathbf{x}}\_{k}^{+} &= \hat{\mathbf{x}}\_{k}^{-} + \mathcal{K}\_{k} \left( \mathbf{z}\_{k} - H\_{k} \hat{\mathbf{x}}\_{k}^{-} \right) \\ P\_{k}^{+} &= \left( I - \mathcal{K}\_{k} H\_{k} \right) P\_{k}^{-} \end{aligned} \tag{6}$$

where, <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** are respectively the predicted (-) and corrected (+) value of the estimated state vector (6x1 vector), ܲ ି and ܲ ା (a 6 x 6 matrix) are respectively the predicted and corrected value of the estimation covariance of the state vector, or:

$$P\_k^+ = \operatorname{diag} \left( \sigma\_{Ox}^2, \sigma\_{Oy'}^2, \sigma\_{Ox'}^2, \sigma\_{Vx'}^2, \sigma\_{Vy'}^2, \sigma\_{Vz}^2 \right)\_k$$

Kk (equation 6) is the Kalman gain (6x6 matrix).

The Kalman filter thus computes a weighted average of the predicted and the measured state vector by using the Kalman gain Kk. If one has an accurate GPS sensor, the uncertainty on the measurement will be small so there will be more weight given to the measurement and thus the corrected estimate will be close to the measurement. When one has a non-accurate sensor, the uncertainty on the measurement is large and more weight will be given to the predicted estimate.

A Kalman smoother has been applied as well where the equations are shown in (7) below. A Kalman Smoother in addition to the past observations also incorporates future observations to estimate the state vector:

$$\begin{aligned} \mathbf{C}\_{k} &= P\_{k}^{+} A\_{k}^{T} \left( P\_{k+1}^{-} \right)^{-1} \\ \hat{\mathbf{x}}\_{k}^{s} &= \hat{\mathbf{x}}\_{k}^{+} + \mathbf{C}\_{k} \left( \hat{\mathbf{x}}\_{k+1}^{s} - A\_{k} \hat{\mathbf{x}}\_{k}^{+} \right) \\ P\_{k}^{s} &= P\_{k}^{+} + \mathbf{C}\_{k} \left( P\_{k+1}^{s} - P\_{k+1}^{-} \right) \mathbf{C}\_{k}^{T} \end{aligned} \tag{7}$$

where:

276 Remote Sensing – Advanced Techniques and Platforms

2

001000 0 0 0 0 0 , , <sup>000100</sup>

000 0 0 <sup>000010</sup> <sup>2</sup>

*A given quantity y is a function of x1, x2, … xN given by the formula y=f(x1,x2,...,xN). The*

*ff f eee e xx x*

... *y N*

� )� equals ���

equals the difference between the position at time k+1 and k, divided by the time interval. It is assumed that there is no uncertainty on the time interval. This is valid because one assumes that **x**k and **x**k+1 are statistically independent. In an similar manner, one can

from ��

The Kalman filter consists of 2 steps. A temporal update step (also known as the "a priori" prediction step) and a measurement update step (also known as the "a posteriori" correction step). In the temporal step given by equations 4 and 5, the estimated state vector <sup>ˆ</sup> *<sup>k</sup>* **<sup>x</sup>** and the

The state vector ˆ *<sup>k</sup>* **x** contains the estimated position of the sensor (Ox,Oy,Oz) (in meters) and

the velocity (Vx,Vy,Vz) (in meters per second) in the reference coordinate system.

1 2

1 2

From the above, one has for the vertical, z direction ���)� <sup>=</sup> ���)�������)�

calculate the covariance of the horizontal velocity, where one assumes

� <sup>=</sup> �� � �

��� � = ���

**3. Kalman filter and smoothing approach**

**3.1 Position and velocity estimations**

The predictive procedure step is given by:

estimation covariance ��

time step k-1.

2 2 2 2 2 2 2

however not given by the GPS but are calculated using using the following, where:

*uncertainties in xi are respectively e1, e2, …, eN. The absolute uncertainty ey is then given by*

2

*h*

100000

covariance of the vertical velocity ����

000001

The covariance's of the vertical and horizontal velocities ���

*k k*

*H R*

010000 2

2

*h*

*k Vh*

2

*v*

2

*Vh*

� and ���

*N*

�)������� �)�

���

� = ���

� � ��� � .

� at time step k are predicted based on the current knowledge at

���

since the velocity at time k

000 0 0

000 0 0

0 00 0 0

00 0 0 0

2

2

2

*Vv k*

� (in m2 per seconds2) are

and hence the

ˆ*s <sup>k</sup>* **x** = the smoothed estimated state vector (6x1 matrix).

ܲ <sup>௦</sup> = the covariance (6x6) matrix of the smoothed estimated state vector.

Ck = the (6x6) matrix that determines the weight of the correction between the smoothed and non-smoothed state.

#### **4. Roll correction**

The second step in the algorithm for motion correction accounts for the influence of the roll motion by using the IMU orientation output. This is not included in the first Kalman filter because the IMU data is given at a higher frequency than the GPS data.

Hyperspectral Remote Sensing –

line.

**wavelength contrast algorithms**

(sensor zenith angle) = 55°,

in the figure below.

hyperspectral imagery.

addition to the wavelength λ, (368-1115 nm) were the

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 279

Fig. 6. This image shows the conversion triangles used to calculate the shiftratio between the shift on the earth surface ss in meters and the pixelshift sp. hH is the altitude above the surface, α half of the angular field of view of the camera and nN the number of pixels in one

Hyperspectral signatures and imagery offer unique benefits in detection of land and water features due to the information contained in reflectance signatures that directly show relative absorption and backscattering features of targets. The reflectance spectra that will be used in this paper were collected *in-situ* on May 31st 2011 using a SE590 high spectral resolution solid state spectrograph and the HSI imaging system described above. Bidirectional Reflectance Distribution Function (BRDF) signatures were collected of weathered oil, turbid water, grass and dead vegetation. The parameters describing the function in

*<sup>i</sup>* (solar azimuth angle) = 105° and the

= 270°. The reflectance BRDF signature is calculated from the downwelling radiance using a calibrated Lambertian diffuse reflectance panel and the upwelling radiance at the above specified viewing geometry for each target (oil, water, grass, dead vegetation) as described

The figures below show the results of measurements from 400 to 900 nm for a 1 mm thick surface weathered oil film, diesel fuel, turbid water (showing the solar induced fluorescence line height feature, dead vegetation, and field grass with the red edge feature common to vegetation and associated leaf surfaces. These BRDF signatures are used below to select optimal spectral channels and regions using optimally selected contrast ratio algorithms in order to discriminate oil from other land & water features in

*<sup>i</sup>* (solar zenith angle) = 71.5°,

<sup>0</sup> (sensor azimuth angle)

0

**6. Feature detection in hyperspectral images using optimal multiple**

The state equations and Kalman filter/smoothing equations are given by 2.5, 2.6 and 2.7 with state vector ' ˆ *<sup>k</sup>* **x** containing the estimated position of the center pixel of the scanline on the surface (in meters) and the tangent of the roll angle (nondimensional) in the reference coordinate system. The measurement vector **z**k' contains the position of the sensor (Ox,Oy) (in meters) specified by the output of the previous Kalman filter in the reference coordinate system and the tangent of the rollangle rm (nondimensional) given by the orientation output of the IMU.

The matrices used are defined by:

$$\begin{aligned} \mathbf{x}'\_k &= \begin{pmatrix} \mathbf{x} \\ \mathbf{y} \\ \mathbf{z} \end{pmatrix}\_k, \quad A'\_k = \begin{pmatrix} \mathbf{1} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{1} & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & \mathbf{1} \end{pmatrix}\_k, \quad Q'\_k = \begin{pmatrix} \mathbf{0}.\mathbf{1} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{0}.\mathbf{1} & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & \mathbf{0}.\mathbf{0}\mathbf{0} \end{pmatrix}\_k \\\\ \mathbf{z}'\_k &= \begin{pmatrix} \mathbf{O}\_x \\ \mathbf{O}\_y \\ \mathbf{0} \\ \tan r\_m \end{pmatrix}\_k, \quad H'\_k = \begin{pmatrix} \mathbf{1} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{1} & h\_{\mathrm{alt}} \\ \mathbf{0} & \mathbf{0} & \mathbf{1} \end{pmatrix}\_k, \quad R'\_k = \begin{pmatrix} \sigma^2\_{\mathrm{Ox}} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \sigma^2\_{\mathrm{Ox}} & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & \sigma^2\_{r} \end{pmatrix}\_k \end{aligned}$$

where:

r = the roll angle (in radians). halt = the altitude of the sensor (in meters) with respect to MSL. ை௫ߪ <sup>ଶ</sup> and ߪை௬ <sup>ଶ</sup> = respectively the covariance of the position in x and y direction of the sensor given by the previous Kalman filter (in m2).

ߪ ଶ = the covariance of the roll angle given by the IMU (nondimensional).

## **5. Image resampling**

In some cases, it is only desirable to cross-track shift corrections and not resample the image in order to keep the pure spectral signatures of measured pixels. Otherwise, 2D nearest neighbourhood resampling is used.

The cross-track shift corrections (which are in the y direction) ss on the surface in meters need to be converted to pixelshifts sp. The number of pixels in one scanline nN, the altitude of the sensor above the surface in meters hH and half of the angular field of view α are used. This is accomplished by defining a conversion ratio cr, the shift in meters on the surface of 1 pixel shift, or:

$$c\_r = \frac{w}{\frac{n\_N}{2}}\tag{8}$$

where:

w = hH tan α.

The pixelshift sp is then given by *<sup>s</sup> <sup>p</sup> r s s c* as depicted below:

The state equations and Kalman filter/smoothing equations are given by 2.5, 2.6 and 2.7 with state vector ' ˆ *<sup>k</sup>* **x** containing the estimated position of the center pixel of the scanline on the surface (in meters) and the tangent of the roll angle (nondimensional) in the reference coordinate system. The measurement vector **z**k' contains the position of the sensor (Ox,Oy) (in meters) specified by the output of the previous Kalman filter in the reference coordinate system and the tangent of the rollangle rm (nondimensional) given by the orientation output

>

, 0 1 0 , 0 0.1 0 tan 001 0 0 0.001

*kk k*

, 01 , 0 0

*<sup>m</sup> <sup>k</sup> <sup>k</sup> <sup>r</sup> <sup>k</sup>*

<sup>ଶ</sup> = respectively the covariance of the position in x and y direction of the sensor

1 0 0 0.1 0 0

10 0 0 0

2

2

*<sup>n</sup>* (8)

2

In some cases, it is only desirable to cross-track shift corrections and not resample the image in order to keep the pure spectral signatures of measured pixels. Otherwise, 2D nearest

The cross-track shift corrections (which are in the y direction) ss on the surface in meters need to be converted to pixelshifts sp. The number of pixels in one scanline nN, the altitude of the sensor above the surface in meters hH and half of the angular field of view α are used. This is accomplished by defining a conversion ratio cr, the shift in meters on the surface of 1

*c*

*r s s*

*c*

2 *<sup>r</sup> <sup>N</sup> w*

as depicted below:

tan 00 1 0 0

*Ox x k y k alt k Oy*

*kk k*

*yA Q*

*O H hR*

ଶ = the covariance of the roll angle given by the IMU (nondimensional).

halt = the altitude of the sensor (in meters) with respect to MSL.

of the IMU.

where:

<sup>ଶ</sup> and ߪை௬

pixel shift, or:

where:

w = hH tan α.

**5. Image resampling**

ை௫ߪ

ߪ

The matrices used are defined by:

**x**

**z**

r = the roll angle (in radians).

neighbourhood resampling is used.

The pixelshift sp is then given by *<sup>s</sup> <sup>p</sup>*

given by the previous Kalman filter (in m2).

*x*

*O*

*r*

*r*

Fig. 6. This image shows the conversion triangles used to calculate the shiftratio between the shift on the earth surface ss in meters and the pixelshift sp. hH is the altitude above the surface, α half of the angular field of view of the camera and nN the number of pixels in one line.

## **6. Feature detection in hyperspectral images using optimal multiple wavelength contrast algorithms**

Hyperspectral signatures and imagery offer unique benefits in detection of land and water features due to the information contained in reflectance signatures that directly show relative absorption and backscattering features of targets. The reflectance spectra that will be used in this paper were collected *in-situ* on May 31st 2011 using a SE590 high spectral resolution solid state spectrograph and the HSI imaging system described above. Bidirectional Reflectance Distribution Function (BRDF) signatures were collected of weathered oil, turbid water, grass and dead vegetation. The parameters describing the function in addition to the wavelength λ, (368-1115 nm) were the *<sup>i</sup>* (solar zenith angle) = 71.5°, 0 (sensor zenith angle) = 55°, *<sup>i</sup>* (solar azimuth angle) = 105° and the <sup>0</sup> (sensor azimuth angle) = 270°. The reflectance BRDF signature is calculated from the downwelling radiance using a calibrated Lambertian diffuse reflectance panel and the upwelling radiance at the above specified viewing geometry for each target (oil, water, grass, dead vegetation) as described in the figure below.

The figures below show the results of measurements from 400 to 900 nm for a 1 mm thick surface weathered oil film, diesel fuel, turbid water (showing the solar induced fluorescence line height feature, dead vegetation, and field grass with the red edge feature common to vegetation and associated leaf surfaces. These BRDF signatures are used below to select optimal spectral channels and regions using optimally selected contrast ratio algorithms in order to discriminate oil from other land & water features in hyperspectral imagery.

Hyperspectral Remote Sensing –

angle from magnetic north direction.

400 500 600 700 800 900 Wavelength (nm)

Contrast

"*multiple-wavelength (or channel) contrast*" as:

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 281

 Fig. 8. Averaged (n=360) BRDF reflectance spectrums collected using a SE590 solid state spectrograph May 31, 2010. From upper left to right: BRDF spectrum of weathered oil (1 mm thick film) on clear water, diesel film (1mm thick film) on clear water, turbid water, with high chlorophyll content as indicated by the solar induced fluorescence line height, dead

Fig. 9. Resulting BRDF Weber contrast signatures between oil as the target and different backgrounds (left to right): turbid water, dead vegetation (dead foliage) and field grass.

0.0

0.5

Contrast

1.0

400 500 600 700 800 900 Wavelength (nm)

400 500 600 700 800 900 Wavelength (nm)

Contrast

A limitation with this common definition of the contrast is that one band is used out of all the possible combinations available in a hyperspectral image for the feature detection or extraction algorithm. This limitation can be overcome, by defining an advantageous

vegetation (dead leaves) and field grass showing the red edge. Solar angles were determined from DGPS location, time of day, and sensor position angles and measured

Fig. 7. Illumination and viewing geometry defined for calculation of the BRDF signatures collected using the 252 channel SE590 high spectral and radiometric sensitivity solid state spectrograph and the hyperspectral imaging system, where θi is the incident solar zenith angle of the sun, θ0 is the sensor zenith angle, Øi is the solar azimuth angle from the north and Ø0 is the sensor azimuth angle as indicated above. In general, a goniometer measurement system is used to measure the BRDF in the field or laboratory environment as the sensor zenith and azimuth angles are changed during a collection period with a given solar zenith conditions.

The above BRDF signatures were used to select optimal spectral regions in order to apply the results to hyperspectral imagery collected from a weathered oil impacted shoreline in Barataria Bay, LA. The first method used was to perform feature detection using spectral contrast signature and HSI image contrast. The well know Weber's contrast definition is first used to determine the maximum (optimal) value of the contrast between a target *t* and a background *b* as a function of wavelength, or:

$$C\_t\left(\mathcal{\lambda}\_k\right) = \frac{BRDF\_t\left(\theta\_0, \phi\_0, \theta\_i, \phi\_i, \mathcal{\lambda}\_k\right) - BRDF\_b\left(\theta\_0, \phi\_0, \theta\_i, \phi\_i, \mathcal{\lambda}\_k\right)}{BRDF\_b\left(\theta\_0, \phi\_0, \theta\_i, \phi\_i, \mathcal{\lambda}\_k\right)}\tag{9}$$

The resulting contrast calculated across the spectrum for each channel are shown below using the 1 mm thick oil film as the target and the backgrounds of turbid water, dead vegetation (dead foliage), and field grass.

The result of the optimization of the contrast obtained from equation 9 yields an optimal channel and/or spectral region as a function of wavelength where the contrast is maximized between a specified target and specified background or feature in a hyperspectral image collected from a fixed or moving platform.

Fig. 7. Illumination and viewing geometry defined for calculation of the BRDF signatures collected using the 252 channel SE590 high spectral and radiometric sensitivity solid state spectrograph and the hyperspectral imaging system, where θi is the incident solar zenith angle of the sun, θ0 is the sensor zenith angle, Øi is the solar azimuth angle from the north

measurement system is used to measure the BRDF in the field or laboratory environment as the sensor zenith and azimuth angles are changed during a collection period with a given

The above BRDF signatures were used to select optimal spectral regions in order to apply the results to hyperspectral imagery collected from a weathered oil impacted shoreline in Barataria Bay, LA. The first method used was to perform feature detection using spectral contrast signature and HSI image contrast. The well know Weber's contrast definition is first used to determine the maximum (optimal) value of the contrast between a target *t* and a

The resulting contrast calculated across the spectrum for each channel are shown below using the 1 mm thick oil film as the target and the backgrounds of turbid water, dead

The result of the optimization of the contrast obtained from equation 9 yields an optimal channel and/or spectral region as a function of wavelength where the contrast is maximized between a specified target and specified background or feature in a hyperspectral image

0 0

*BRDF BRDF*

0 0 0 0

*b ii k*

, ,,, , ,,, , ,,, *t ii k b ii k*

*BRDF* (9)

and Ø0 is the sensor azimuth angle as indicated above. In general, a goniometer

solar zenith conditions.

background *b* as a function of wavelength, or:

*t k*

vegetation (dead foliage), and field grass.

collected from a fixed or moving platform.

*C*

Fig. 8. Averaged (n=360) BRDF reflectance spectrums collected using a SE590 solid state spectrograph May 31, 2010. From upper left to right: BRDF spectrum of weathered oil (1 mm thick film) on clear water, diesel film (1mm thick film) on clear water, turbid water, with high chlorophyll content as indicated by the solar induced fluorescence line height, dead vegetation (dead leaves) and field grass showing the red edge. Solar angles were determined from DGPS location, time of day, and sensor position angles and measured angle from magnetic north direction.

Fig. 9. Resulting BRDF Weber contrast signatures between oil as the target and different backgrounds (left to right): turbid water, dead vegetation (dead foliage) and field grass.

A limitation with this common definition of the contrast is that one band is used out of all the possible combinations available in a hyperspectral image for the feature detection or extraction algorithm. This limitation can be overcome, by defining an advantageous "*multiple-wavelength (or channel) contrast*" as:

Hyperspectral Remote Sensing –

Using Low Flying Aircraft and Small Vessels in Coastal Littoral Areas 283

 <sup>2</sup> <sup>1</sup> <sup>4</sup> 0.5 0.5

 <sup>2</sup> 0.5 0.25 1 1

To order to give equal weight to absorption and backscatter features in the band selection

*I for I*

<sup>1</sup> 0 1

*for I <sup>I</sup>*

Using this definition, both concavities will be on the same scale and a concave-down feature (hence: backscattering) will give a positive value (>1) while a concave-up feature (hence: absorption) will give a negative value (<-1) and be treated the same numerically. For example, in the above example, the result for the new definition of the inflection or 2nd

A second issue is to determine what values to assign to the upward and backward operators in the dilation filter. One could pick the optimal value for the inflection using all possible combinations of m and n. The problem with this method is that when m and n are large, the difference between the channels for which the inflection is calculated and the one to which it is compared can be influenced by the signal to noise ratio being at the low and high wavelengths in a typical camera/spectrograph system. Thus the resulting optimal regions selected can be scientifically or physically difficult to explain. Thus a limit is placed on the maximum value of the m, n operators from a practical point of view. The minimal value of m, n is 1. Thus, one can select the optimal range of the m and n wavelet filter operators (either a maximum (backscattering) or a minimum (absorption) for all combinations of m and n between 1 and the maximal value (in this paper this maximal value used was selected as 7). The resulting derivative estimator spectra (inflection spectra) using equation 12 was calculated and is shown

Fig. 11. BRDF Inflection spectra using the reflectance spectrums above. From left to right: an oil film (1mm thick) on clear water, turbid water, dead vegetation (dead leaves) and grass.

Inflection of dead vegetation

1 2

400 500 600 700 800 900 Wavelength (nm)

The inflection algorithm can also be applied to the contrast spectrums (to enhance variation in the contrast spectrum). The result of this calculation is given in the following figures.

1

(12)

400 500 600 700 800 900 Wavelength (nm)

Inflection of grass

*I*

*I*

below, using the previously shown BRDF spectra shown in Figure 8 above.

400 500 600 700 800 900 Wavelength (nm)

Inflection of turbid water

1 2 \*

*I*

While in the case of the concave-up (same concavity), the result will be:

process, a modified spectrum for I\*(λ) is defined as:

derivative estimator would be 4 and -4.

400 500 600 700 800 900 Wavelength (nm)

Inflection of oil

1 2

$$\begin{split} \mathbf{C}\_{t}\left(\boldsymbol{\lambda}\_{k,m}\right) &= \frac{BRDF\_{t}\left(\boldsymbol{\theta}\_{0},\boldsymbol{\phi}\_{0},\boldsymbol{\theta}\_{i},\boldsymbol{\phi}\_{i},\boldsymbol{\lambda}\_{k}\right) - BRDF\_{b}\left(\boldsymbol{\theta}\_{0},\boldsymbol{\phi}\_{0},\boldsymbol{\theta}\_{i},\boldsymbol{\phi}\_{i},\boldsymbol{\lambda}\_{k\pm m}\right)}{BRDF\_{b}\left(\boldsymbol{\theta}\_{0},\boldsymbol{\phi}\_{0},\boldsymbol{\theta}\_{i},\boldsymbol{\phi}\_{i},\boldsymbol{\lambda}\_{k\pm m}\right)} \\ &= \frac{BRDF\_{t}\left(\boldsymbol{\theta}\_{0},\boldsymbol{\phi}\_{0},\boldsymbol{\theta}\_{i},\boldsymbol{\phi}\_{i},\boldsymbol{\lambda}\_{k}\right)}{BRDF\_{b}\left(\boldsymbol{\theta}\_{0},\boldsymbol{\phi}\_{0},\boldsymbol{\theta}\_{i},\boldsymbol{\phi}\_{i},\boldsymbol{\lambda}\_{k\pm m}\right)} - 1 \end{split} \tag{10}$$

The result of the optimization of this "*multiple-wavelength contrast algorithm*" is the optimal selection of a band ratio (located in a spectral region) minus one. Furthermore, a new definition of the inflection contrast spectrum (a numerical approximation of the second derivative) can be defined. The contrast inflection spectrum described in previous papers was given by:

$$I\_t\left(\mathcal{J}\_{k,m,n}\right) = \frac{BRDF\_t\left(\theta\_0, \phi\_0, \theta\_i, \phi\_i, \mathcal{J}\_k\right)^2}{BRDF\_t\left(\theta\_0, \phi\_0, \theta\_i, \phi\_i, \mathcal{J}\_{k\times m}\right)BRDF\_t\left(\theta\_0, \phi\_0, \theta\_i, \phi\_i, \mathcal{J}\_{k-n}\right)}\tag{11}$$

where *m* and *n* are respectively defined as a dilating wavelet filter forward and backward operators described by Bostater, 2006. This inflection is used to estimate the second derivative of reflectance spectra. The underlying goal of computing an approximation of the second derivative is to utilize the nonlinear derivative based, dilating wavelet filter to enhance the variations in the reflectance spectra signals, as well as in the contrast spectrum signals. These variations directly represent the target and background absorption (hence: concave up) and backscattering (hence: concave down) features within a hyperspectral reflectance image or scene and form the scientific basis of the discrimination based noncontact optimal sensing algorithms. A practical limitation encountered using this definition above, is that a concave-down (or backscattering) feature value of the inflection as defined in 2.7 is greater than one and a concave up (or absorption) feature, in the inflection or derivative based wavelet filter defined in 1.7 will be between 0 and 1. There is thus a difference in scale between a concave-up and a concave-down behavior. Consider the following example:

Fig. 10. Example concave-down (backscattering) feature (blue line) and a concave-up (absorption) feature (red line) of the same amplitude (Y axis) as a function of an spectral wavelength on the x axis.

In the case of the concave-down, the result of the inflection is:

*t ii k b i i km*

*BRDF BRDF*

*BRDF*

The result of the optimization of this "*multiple-wavelength contrast algorithm*" is the optimal selection of a band ratio (located in a spectral region) minus one. Furthermore, a new definition of the inflection contrast spectrum (a numerical approximation of the second derivative) can be defined. The contrast inflection spectrum described in previous papers

Fig. 10. Example concave-down (backscattering) feature (blue line) and a concave-up (absorption) feature (red line) of the same amplitude (Y axis) as a function of an spectral

0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.4

In the case of the concave-down, the result of the inflection is:

0.5 0.6 0.7 0.8 0.9 1.0 1.1 *BRDF*

*BRDF BRDF*

*C*

was given by:

following example:

wavelength on the x axis.

*t km*

*I*

*t kmn*

, ,

,

where *m* and *n* are respectively defined as a dilating wavelet filter forward and backward operators described by Bostater, 2006. This inflection is used to estimate the second derivative of reflectance spectra. The underlying goal of computing an approximation of the second derivative is to utilize the nonlinear derivative based, dilating wavelet filter to enhance the variations in the reflectance spectra signals, as well as in the contrast spectrum signals. These variations directly represent the target and background absorption (hence: concave up) and backscattering (hence: concave down) features within a hyperspectral reflectance image or scene and form the scientific basis of the discrimination based noncontact optimal sensing algorithms. A practical limitation encountered using this definition above, is that a concave-down (or backscattering) feature value of the inflection as defined in 2.7 is greater than one and a concave up (or absorption) feature, in the inflection or derivative based wavelet filter defined in 1.7 will be between 0 and 1. There is thus a difference in scale between a concave-up and a concave-down behavior. Consider the

0 0

0 0 0 0

, ,,, <sup>1</sup> , ,,,

0 0

0 0 0 0

*t i i km t i i kn*

*BRDF BRDF* (11)

2

, ,,, , ,,, , ,,, *t ii k*

, ,,, , ,,, , ,,,

*t i i k b i i km*

*b i i km*

(10)

$$I = \frac{1^2}{0.5 \ast 0.5} = 4$$

While in the case of the concave-up (same concavity), the result will be:

$$I = \frac{0.5^2}{1 \ast 1} = 0.25$$

To order to give equal weight to absorption and backscatter features in the band selection process, a modified spectrum for I\*(λ) is defined as:

$$I^\* = \begin{cases} \begin{array}{c} I \\ -\frac{1}{I} \end{array} & \text{for } I > 1 \\ -\frac{1}{I} \end{array} \tag{12}$$

Using this definition, both concavities will be on the same scale and a concave-down feature (hence: backscattering) will give a positive value (>1) while a concave-up feature (hence: absorption) will give a negative value (<-1) and be treated the same numerically. For example, in the above example, the result for the new definition of the inflection or 2nd derivative estimator would be 4 and -4.