This chapter provides a proof of concept for how a key staple crop can be monitored on a national scale using existing remote-sensing (RS) products that will soon be complemented and superseded by forthcoming sensors. Our exemplar crop is rice and our test country is Bangladesh (Figure 1). This region is highly suitable for a demonstration because of the triple remote-sensing challenge of pervasive cloud cover, small field size, and complex cropping patterns, which are typical of the vast and important agricultural areas of Asia, Africa, and Latin America. It is these areas where future gains in productivity must and will be made, not the agricultural areas of Europe, the US, and other developed regions where crop monitoring

We first briefly describe the rice environments of Bangladesh, and then demonstrate how a combination of hypertemporal synthetic-aperture radar (SAR) and optical RS data can be combined to generate both baseline map information and near–real-time monitoring infor‐

Agriculture is one of the most important sectors in Bangladesh's economy, accounting for approximately 20% of the gross domestic product (GDP) in 2010. It also accounts for the employment of over 60% of the country's population [2]. Rice is far and away the most important crop in the agricultural sector. Almost 60% of the land area is planted to rice and it provides food, employment, and income for much of the rural population. Bangladesh is one of the most populous countries in Asia, with almost 150 million people, according to the 2011 census. With over 1,000 people per square kilometre, it is one of the most densely populated countries in the world [2]. The country's milled rice consumption per capita is the highest in

Rice can be cultivated in any of three seasons in Bangladesh in a myriad of cropping systems such as rice/pulse, rice/maize, rice/wheat, rice/vegetables, rice/shrimp and double-or triplerice monoculture. The dry *boro* season, which runs from November to April, is largely dependent on irrigation and is of growing importance to Bangladesh's rice output. This is followed by the *aus* season (sometimes referred to as early *kharif*), which runs from March to

the world at 173 kg per year or almost 500 g of uncooked rice per day [1].

way to reduce the likelihood of similar events in the future.

is substantially easier.

122 Land Applications of Radar Remote Sensing

**2. Rice in Bangladesh**

mation on crop extent and crop seasonality.

processing, validating, and delivering the information are considerable.

August and relies on irrigation to establish the crop and early monsoon rains during the rest of the season. The *aman* season from June to December relies almost entirely on monsoon rains and is another major source of rice production. Deepwater aman rice grown in flooded conditions using tall varieties or floating rice starts in April or May, but is harvested in November/December. Figure 2 shows how these seasons overlap due to the large variation in (trans)planting and harvesting windows across the country. In general, the boro season starts in the east and south and moves northward; whereas, the rainfed aman season starts in the northernmost reaches of the delta and moves from north to south, although, local conditions, cropping patterns, and other management decisions can affect this trend.

Figure 3 shows the trends in rice cropped area per season for 1999-2012 and Figure 4 shows the reported area for each of the 64 districts in 2011 based on BBS annual reports [2-3]. Figure 3 shows the general stability in total rice area with extremes of 10.4 (2004) and 11.5 (2011) million hectares; the recent increases are mainly due to the expansion of the area in the *boro* season. Figure 4 shows the general spatial distribution of rice per season where the boro crop is mainly in the north and east, the much smaller aus crop area is in various clusters across the country, and the aman crop is dominant in both north and southern coastal regions.

3

4

7

1 clusters across the country, and the aman crop is dominant in both north and southern 2 coastal regions. **Figure 2.** Generic rice crop calendar for Bangladesh (from FAO).

6 **Figure 3.** Bangladesh rice area by season 1999-2012 (Source: [2-3]).

5 Figure 3. Bangladesh rice area by season 1999-2012 (Source: [2-3]).

8 Figure 4. Bangladesh rice area per district in 2011 (l to r: boro, aus, aman) (Source: [3]). **Figure 4.** Bangladesh rice area per district in 2011 (l to r: boro, aus, aman) (Source: [3]).

Whilst production has increased steadily over the past 10 years, this trend faces multiple challenges. Flooding from excessive rainfall and the massive inflow from the Ganges (called Padma in Bangladesh), Brahmaputra (called Jamuna in Bangladesh), and Meghna rivers is estimated to severely affect 1.32 million ha and moderately affect a further 5.05 million ha. Crop losses from such massive inundation can be enormous. At the same time, drought brought on by a shortage of rainfall affects both the rainfed aman crop and the dry season crops, with a total drought-affected area estimated at 3.52 million ha. Salinity and tidal surges from cyclones affect a further 1 million ha of cultivated land, and any given area can suffer from one or more of these stresses during the year [4]. Severe yield losses are also attributed to pests, diseases, weeds, and other biotic yield reducers.

The possible impacts, both positive and negative, of changes in climate, society, economy and technology are hard to quantify, but the fundamental message is that Bangladesh faces multiple challenges to food production which are the focus of ongoing research and develop‐ ment across the country, such as stress tolerant varieties, innovative cropping systems, investments in infrastructure, extension and training. Bangladesh has been identified as one the countries with the highest vulnerability to anticipated climate change and there is contin‐ ued need to deliver the best possible crop status information to policy makers as a contribution to sustainable and resilient agricultural production.

In summary, rice can be cultivated during any month and it faces different pressures in each of the three seasons. Rice is cultivated in varying amounts in every district with differing spatial patterns each season.  For this reason, hypertemporal imagery with a large footprint, such as MODIS and ENVISAT WS products, are good choices for baseline rice extent mapping and rice seasonality monitoring. They also serve as a proof of concept of what can be achieved at higher spatial and temporal resolutions with future satellite programmes in an operational monitoring context.

### **3. Methods and data**

4 Book Title

**Bangladesh rice area per season (million ha)**

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

boro aus aman

1 clusters across the country, and the aman crop is dominant in both north and southern

5 Figure 3. Bangladesh rice area by season 1999-2012 (Source: [2-3]).

**Figure 3.** Bangladesh rice area by season 1999-2012 (Source: [2-3]).

**Figure 2.** Generic rice crop calendar for Bangladesh (from FAO).

8 Figure 4. Bangladesh rice area per district in 2011 (l to r: boro, aus, aman) (Source: [3]).

**Figure 4.** Bangladesh rice area per district in 2011 (l to r: boro, aus, aman) (Source: [3]).

2 coastal regions.

124 Land Applications of Radar Remote Sensing

3

4

6

7

### **3.1. Multi-temporal SAR and optical data for rice mapping and monitoring**

Field conditions and crop evolution follow well understood seasonal changes, which can be observed by multi-temporal remote-sensing data. Combined with a priori knowledge of the crop calendar and land practices, this multi-temporal remote-sensing data can be correctly interpreted to deliver valuable information. For rice, at the start of the season, remote-sensing time-series can determine when and where fields are prepared and irrigated. The same timeseries information can also capture the various crop stages, starting from seeding or trans‐ planting and then through the vegetative, reproductive, and ripening stages until harvest. In synthesis, assuming that images are regularly acquired, the key source of information about crop presence and status is the temporal signature [5-9]. Systematic acquisitions are essential; one image or randomly acquired scenes, even with very high resolution, are of limited use for crop monitoring.

SAR systems have a proven ability to detect irrigated and lowland rice through the unique backscatter temporal signature. In the past three decades, a significant number of publications have been dedicated to the rice signature and to its detection and monitoring [10-17]. In summary, these studies have shown that:

High-resolution optical images in the tropics are strongly limited for crop monitoring pur‐ poses, especially rice. Most rice is grown in the rainy or monsoon season and images often suffer from persistent and widespread cloud contamination. High-resolution images are rarely available with high temporal frequency, so gap-filling and smoothing options are limited. On the contrary, moderate-resolution systems with daily revisiting cycles, such as MODIS, have the appropriate spectral bands and temporal resolution needed for crop identification and phenological monitoring at regional scales.

Although sometimes neglected and rarely the subject of discussion in remote-sensing litera‐ ture, the impact of meteorological conditions and crop practices on rice plant growth is often significant. Some prior knowledge of the rice variety, the crop calendar, varietal maturity, crop management practices for water and inputs, and meteorological conditions is prerequisite for the correct interpretation of the temporal signature and for the generation of an accurate 'rice map', whatever the specific information (i.e., rice area, start of season date, etc.) in the map. This agronomic and meteorological information must be considered in the development of a rice detection algorithm.

Thus, there is an opportunity to use both SAR and optical time-series information for rice crop detection and monitoring, provided that there is sufficient a priori knowledge of the rice system under observation. The following approach is based on specific peculiarities and complementarities of high temporal resolution and moderate spatial resolution SAR and optical data; however, the approach can also use higher resolution imagery if available.

SAR systems have a proven ability to detect irrigated and lowland rice through the unique backscatter temporal signature. In the past three decades, a significant number of publications have been dedicated to the rice signature and to its detection and monitoring [10-17]. In

**•** Lower frequencies (L-and C-band) penetrate deeper into the rice plant than higher frequen‐

**•** The correlation between the backscattering coefficient and rice bio-physical parameters shows that, lower frequencies are more related to the total fresh weight, leaf area index, and plant height; whereas higher frequencies better correlate with grain weight and grain water

**•** The date of the maximum backscattering coefficient at X-band precedes those of C-and L-

**•** The VV backscattering coefficient increases only during the vegetative stage; it is quite stable at the reproductive stage, and it decreases at the ripening stage due to the canopy attenua‐

**•** The HH backscattering coefficient increases at the reproductive stage, and it is quite stable at the ripening stage. The temporal trend of the HV backscattering coefficient is similar to

**•** The VV/HH polarization ratio, at C-and L-band, significantly decreases throughout the

**•** The frequency ratios for HH and VV (C-VV/L-VV and C-HH/L-HH) are significantly lower in the latter part of the rice season, when thick vegetation canopy hampers wave penetration.

High-resolution optical images in the tropics are strongly limited for crop monitoring pur‐ poses, especially rice. Most rice is grown in the rainy or monsoon season and images often suffer from persistent and widespread cloud contamination. High-resolution images are rarely available with high temporal frequency, so gap-filling and smoothing options are limited. On the contrary, moderate-resolution systems with daily revisiting cycles, such as MODIS, have the appropriate spectral bands and temporal resolution needed for crop identification and

Although sometimes neglected and rarely the subject of discussion in remote-sensing litera‐ ture, the impact of meteorological conditions and crop practices on rice plant growth is often significant. Some prior knowledge of the rice variety, the crop calendar, varietal maturity, crop management practices for water and inputs, and meteorological conditions is prerequisite for the correct interpretation of the temporal signature and for the generation of an accurate 'rice map', whatever the specific information (i.e., rice area, start of season date, etc.) in the map. This agronomic and meteorological information must be considered in the development of a

course of the crop season, hence proving its sensitivity to rice plant age.

**•** Double bounce dominates at L-band, whereas volume scattering prevails at X-band.

cies, whereas higher frequencies (X-band) interact better with rice grains.

summary, these studies have shown that:

126 Land Applications of Radar Remote Sensing

phenological monitoring at regional scales.

rice detection algorithm.

content.

band.

tion.

the HH one.

Given the higher spatial resolution but limited revisiting time (ranging from 16 to 24 days), SAR data are primarily used to estimate the spatial extent (resolution 100 m) of the rice crop. Medium-resolution optical data with a quasi-daily revisiting cycle are used to derive the land surface phenology as a means to identify key stages of the rice crop season. There are two distinct processing chains to develop rice monitoring products on an operational basis: one for the rice extent/area estimation from SAR, another for rice seasonal monitoring within that extent/area from MODIS. The main advantages of this approach are:

Figure 5 illustrates the overall approach. Depending upon data availability, rice products based on archive and actual remote-sensing data at different spatial scales can be generated. Here, we will discuss the use of archive ENVISAT ASAR Wide Swath data (100 m) and MODIS (500 m), leading to a multi-year rice extent and phenological monitoring product.

**Figure 5.** Products and supported sensors (past, current, near future) including acquisition modes.

### **3.2. Multi-year rice extent from archive ENVISAT ASAR Wide Swath data**

The C-band Advanced Synthetic Aperture Radar (ASAR) on board of the ENVISAT satellite ensured continuity of the ERS-1/2 SAR systems between 2002 and 2012. It featured enhanced capability in terms of coverage, range of incidence angles, polarisation, and modes of opera‐ tion. There were three ASAR acquisition modes with a 35-day repeat cycle:

The 10-year ENVISAT ASAR WS data archive is mainly based on background mission acquisitions, hence, it was not specifically planned and not optimised for agricultural appli‐ cations. Nevertheless, the large archive can be exploited for rice mapping with appropriate processing and interpretation. A fully automated processing chain has been developed to convert the images into terrain geocoded backscattering coefficient (σ°).

**5.** Removal of atmospheric attenuation – Although microwave signals have the ability to penetrate through the clouds, it is possible, particularly at short (X-and C-band) wave‐ lengths, that severe localised storms can affect the backscattering coefficient in the range of several dB. The temporal signature of the backscatter coefficient can be affected in two ways: (1) the thick layer of water vapor generates a strong decrease of backscattering coefficient, followed by a strong increase; (2) the strong rainfall generates a strong increase of the backscattering coefficient, followed by a strong decrease. These effects are corrected in the processing chain by analyzing the temporal signature—anomalous peaks or troughs are identified and the backscattering coefficient values are corrected by means of an interpolator. This correct application of this process relies strongly on a priori knowledge of the rice system and the weather conditions when the image was acquired.

**3.2. Multi-year rice extent from archive ENVISAT ASAR Wide Swath data**

tion. There were three ASAR acquisition modes with a 35-day repeat cycle:

**•** Wide Swath mode-WS (400 by 400 km, 100 m resolution, HH or VV),

**•** Image mode – IM (100 by 100 km, 15-25 m resolution, HH or VV), and,

convert the images into terrain geocoded backscattering coefficient (σ°).

in order to compensate for the range dependency.

VH).

128 Land Applications of Radar Remote Sensing

filtering.

radar.

The C-band Advanced Synthetic Aperture Radar (ASAR) on board of the ENVISAT satellite ensured continuity of the ERS-1/2 SAR systems between 2002 and 2012. It featured enhanced capability in terms of coverage, range of incidence angles, polarisation, and modes of opera‐

**•** Alternating Polarization – AP (100 by 100 km, 15-25 m resolution, HH/VV or HH/HV or VV/

The 10-year ENVISAT ASAR WS data archive is mainly based on background mission acquisitions, hence, it was not specifically planned and not optimised for agricultural appli‐ cations. Nevertheless, the large archive can be exploited for rice mapping with appropriate processing and interpretation. A fully automated processing chain has been developed to

**1.** Co-registration – Images acquired with the same observation geometry and mode are coregistered in slant range geometry. This step is mandatory to allow time-series speckle

**2.** Time-series speckle filtering – An optimum multi-temporal filtering is used to balance differences in reflectivity between images at different times [18]. Multi-temporal filtering is based on the assumption that the same resolution element on the ground is illuminated by the radar beam in the same way, and corresponds to the same slant range coordinates in all images of the time series. The reflectivity can change from one time to the next due to a change in the dielectric and geometrical properties of the elementary scatters, but should not change due to a different position of the resolution element with respect to the

**3.** Terrain geocoding, radiometric calibration, and normalization – A backward solution by considering a Digital Elevation Model is used to convert the positions of the backscatter elements into slant range image coordinates. A range-Doppler approach is used to tranform the three-dimensional object coordinates in a cartographic reference system into the two-dimensional row and column coordinates of the slant range image. During this step, the radiometric calibration is performed by means of the radar equation, where scattering area, antenna gain patterns, and range spread loss are considered. Finally, the backscattering coefficient is normalized according to the cosine law of the incidence angle

**4.** Anisotropic Non-Linear Diffusion Filtering – This filter significantly smoothes homoge‐ neous targets, whilst also enhancing the difference between neighbouring areas. The filter uses the diffusion equation, where the diffusion coefficient, instead of being a constant scalar, is a function of image position and assumes a tensor value [19]. In this way, it is

locally adapted to be anisotropic close to linear structures such as edges or lines.

Once the WS archive has been processed, it is ready for interpretation. Rice detection relies on interpretation of the temporal signature from regular acquisitions within a given year or season. The 2002-2012 WS archive does not have regular acquisitions over any one area and so, we made the following assumption. Given that rice extent in Asia is relatively constant (see Figure 3 for evidence of this in Bangladesh) and cropping calendars are relatively stable, we can then develop a pseudo-annual time-series with a relatively high temporal occurrence by combining all years of WS observations into one year. This is illustrated in Figure 6 where the multi-year SAR data for a selected time frame (e.g., weekly, bi-monthly) is temporally averaged leading to an annual signature.

**Figure 6.** Example of the pseudo-annual time series for a single pixel based on multi-year ASAR Wide Swath data (2002-2010).

This temporal signature can be interpreted in two ways:

The most representative temporal features for rice have been found to be the relative minimum and maximum, their difference, and minimum and maximum increment between two subsequent acquisitions. The selected five temporal features are used to generate rice extent and rice seasons according to:

	- **•** conditions 1 and 2 are satisfied;
	- **•** the range between relative minimum and maximum attains a minimum value;
	- **•** the temporal duration between 1 and 2 is within a given duration;

### **3.3. Phenological monitoring with MODIS data**

### *3.3.1. MODIS data for land surface monitoring*

With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA TERRA (EOS AM-1) satellite, a new era in multi-spectral satellite remote sensing began. MODIS sensors permit continuous monitoring of the environment by measuring spectral bands from the blue to the thermal infrared. The 36-band MODIS spectrometer provides a global data set every 1-2 days. The swath dimensions of MODIS are 2,330 x 2,330 km and the spatial resolution (pixel size at nadir) is 250 m for channels 1 and 2 (0.6 µm-0.9 µm), 500 m for channels 3 to 7 (0.4 µm-2.1 µm) and 1,000 m for channels 8 to 36 (0.4 µm-14.4 µm).

There are many standard MODIS data products that are provided to users in near–real-time and easy-to-use formats for no cost. The MOD09A1 product (a full technical description is available online at https://lpdaac.usgs.gov/products/modis\_products\_table/mod09a1) pro‐ vides 8-day composite reflectance data across seven spectral bands (red-0.6 µm, NIR-0.9 µm, blue-0.4 µm, green-0.5µm, NIR-1.2 µm, SWIR-1.6 µm, SWIR-2.1 µm) at 500 m spatial resolu‐ tion, as well as pixel-specific quality control flags and observation dates. This product type is derived from a multi-step process that considers atmospheric, cloud, and aerosol corrections, and records the best reflectance data registered during the time composite window for each pixel. MODIS product data are provided in Hierarchical Data Format (HDF) on a tile system with the Sinusoidal projection grid. Each tile of the grid covers an area of 1,200 × 1,200 km, more or less 10° of latitude and longitude (Figure 7A). Despite drawbacks related to spatial and spectral resolution, it is the zero cost, ease of access, clarity of product description, and ready-to-use nature of MODIS products that have contributed to their widespread use as a source of remote-sensing information for monitoring.

**Figure 7.** The study area: The location of MODIS tile H26V06 in the Global Sinusoidal projection map (A), tile H26V06 which covers most of Bangladesh (B), and, Bangladesh in geographic coordinates showing the extent of H26V06, which covers all the country except the far the north west (C).

#### *3.3.2. PhenoRice algorithm*

**1.** The temporal evolution of the backscattering coefficient is analyzed from an agronomic perspective. This assumes that the data have been regularly acquired in temporal terms, and that a priori knowledge of rice type, calendar and duration, crop practice, and meteorological conditions during the whole season is available. This is not the case for the

**2.** From the backscattering coefficient time series, representative temporal features (or descriptors) can be derived. Even if this approach cannot derive specific information on the rice phenology [9], it can provide valuable information on the rice location and, depending upon the temporal frequency of the SAR acquisitions, the various rice seasons.

The most representative temporal features for rice have been found to be the relative minimum and maximum, their difference, and minimum and maximum increment between two subsequent acquisitions. The selected five temporal features are used to generate rice extent

**1.** The start of the rice season is identified when there is a relative minimum followed by a

**2.** The peak of the rice season is identified when there is a relative maximum followed by a

**•** the range between relative minimum and maximum attains a minimum value;

**4.** Rice is further distinguished in single, double, triple, etc., according the number of valid

With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA TERRA (EOS AM-1) satellite, a new era in multi-spectral satellite remote sensing began. MODIS sensors permit continuous monitoring of the environment by measuring spectral bands from the blue to the thermal infrared. The 36-band MODIS spectrometer provides a global data set every 1-2 days. The swath dimensions of MODIS are 2,330 x 2,330 km and the spatial resolution (pixel size at nadir) is 250 m for channels 1 and 2 (0.6 µm-0.9 µm), 500 m for channels 3 to 7 (0.4 µm-2.1 µm) and 1,000 m for channels 8 to 36 (0.4 µm-14.4

There are many standard MODIS data products that are provided to users in near–real-time and easy-to-use formats for no cost. The MOD09A1 product (a full technical description is

**•** the temporal duration between 1 and 2 is within a given duration;

We use this approach for interpreting the signature from the WS archive.

maximum increment between two subsequent acquisitions;

minimum increment between two subsequent acquisitions;

WS archive, so we move to option two.

and rice seasons according to:

130 Land Applications of Radar Remote Sensing

**3.** The pixel is classified as rice if:

**•** conditions 1 and 2 are satisfied;

minima and maxima (condition 3).

*3.3.1. MODIS data for land surface monitoring*

µm).

**3.3. Phenological monitoring with MODIS data**

MOD09A1 data for 2011 from the tile H26V06 (Figure 7B) were used to demonstrate seasonal rice monitoring in Bangladesh (Figure 7C) using a rule-based automatic algorithm called "PhenoRice." The aim of the PhenoRice approach is to detect paddy rice by analysing multispectral data with quasi-daily revisiting cycles (e.g., TERRA/AQUA-MODIS, SPOT-VGT, etc.) in a consistent and flexible way that minimises dependency on local threshold adaptation.

The basis for this approach is described in previous publications by CNR-IREA [7,20,21]. Before any seasonal phenological monitoring can be performed, PhenoRice needs an estimate of the rice-growing area. If there is no rice extent map available, then the PhenoRice process relies on the work of [6,22-23] which identifies irrigated or lowland rainfed rice when a clear and unambiguous agronomic flood is detected and is shortly followed by a rapid increase in vegetation growth. However, the process can also use available land cover maps that show rice extent, or use multi-year rice extent maps such as in this case study. The seasonal pheno‐ logical monitoring part of PhenoRice is performed by analysing the temporal signatures of various vegetation indices [20].

Figure 8 shows a schematic diagram illustrating the concept and steps of PhenoRice algorithm. The algorithm involves three fundamental processing steps.

#### **1.** Pre-processing of MODIS composite data

Figure 8 shows a schematic diagram illustrating the concept and steps of PhenoRice algorithm.

The algorithm involves three fundamental processing steps.

132 Land Applications of Radar Remote Sensing

**Figure 8.** PhenoRice flow chart.

**1.** Pre-processing of MODIS composite data

	- **a.** Rice transplanting/seeding (MIN point) is identified when a local EVI minima occurs at the same time as a flood (NDWI, LSWI), and a series of positive EVI derivative values (indicating plant growth) occur shortly after.
	- **b.** Rice heading/flowering (MAX point) is detected when there is a local absolute maxima in the EVI time-series.
	- **c.** Finally, following [7], the smoothed EVI signal is analysed to extract rice emergence [start of season (SoS)] and maturity [end of season (EoS)]. These metrics are identified for each rice pixel, when EVI values match pixel-specific relative thresholds.

All the PhenoRice outputs refer to a temporal window of 12 months from 1 January to 31 December with a temporal step of 8 days corresponding to a MODIS composite granularity. For the year under analysis, referred to as Current Year (CY), the method is able to provide information on the different rice crop seasons (up to three) that occur in the period. It is assumed that a rice season belongs to the given CY, if at least the maximum (i.e., heading) of the crop occurs in this period (from January to December).

In order to provide a flexible method able to detect rice seasons in different environmental and climatic conditions with a specific crop calendar, we assume that the start of the season (flooding and seeding/transplanting) can occur in the previous calendar year (PY) up to six months before the start of CY. This results in an 18-month analysis period, which corresponds to 24 8-day MOD09A1 composites (192 days) in the PY and 46 8-day composites in the CY under analysis.

Finally, phenological metrics maps are produced for each quarter of the year. A rice detection is therefore attributed to the quarter, in which heading occurs. Consequently, the associated MIN, SoS, and EoS detection are referred to the same quarter, but as separate maps. In the case study of Bangladesh, output maps have been post-processed and synthesized for the specific cropping seasons of the country. Following the knowledge of the local crop calendar, all the detection that occurs between February and late April are attributed to boro, the ones between May and late July to aus, and others in the period from August to late November to aman.

### **4. Results**

### **4.1. Multi-year rice extent from the ENVISAT WS archive**

The 2002-2010 ASAR Wide Swath data stack consists of around 340 frames. Four scenes (in two different orbits) were needed to completely cover the country. Hence, on average, there are 85 frames across the 8-year period, corresponding to around 10 images per month in the pseudo-annual time-series approach (Figure 6). In practice, the amount of images per month in the pseudo-annual stack varies between 3 and 16, meaning that the archive data is not suitable for annual or regular monitoring purposes. Figure 9A illustrates the country coverage mosaic of three temporal features: the relative minimum, the relative maximum, and the maximum variation.

The three temporal features, illustrated as a mosaic in Figure 9A, are exclusively carried out for visualization purposes. Indeed, the applied gradient algorithm, which is used to produce a seamless image, may significantly change the radiometric content in the overlapping areas; hence, it is not suitable for data analysis purposes. Looking at the temporal features at country level (Figure 9A), three main colours can be identified: blue, yellow, and light and dark orange to brown. The blue colour represents the minimum, i.e., a constant low radar backscatter during the whole year. Yellow, identified as rice and primarily located in the northeast of Bangladesh, is the combination of a strong temporal variation (red) and a high maximum (green). Light and dark orange to brown, distributed over the whole country, classified as rice, is essentially a combination of a strong temporal variation (red) with a medium maximum (green).

assumed that a rice season belongs to the given CY, if at least the maximum (i.e., heading) of

In order to provide a flexible method able to detect rice seasons in different environmental and climatic conditions with a specific crop calendar, we assume that the start of the season (flooding and seeding/transplanting) can occur in the previous calendar year (PY) up to six months before the start of CY. This results in an 18-month analysis period, which corresponds to 24 8-day MOD09A1 composites (192 days) in the PY and 46 8-day composites in the CY

Finally, phenological metrics maps are produced for each quarter of the year. A rice detection is therefore attributed to the quarter, in which heading occurs. Consequently, the associated MIN, SoS, and EoS detection are referred to the same quarter, but as separate maps. In the case study of Bangladesh, output maps have been post-processed and synthesized for the specific cropping seasons of the country. Following the knowledge of the local crop calendar, all the detection that occurs between February and late April are attributed to boro, the ones between May and late July to aus, and others in the period from August to late November to aman.

The 2002-2010 ASAR Wide Swath data stack consists of around 340 frames. Four scenes (in two different orbits) were needed to completely cover the country. Hence, on average, there are 85 frames across the 8-year period, corresponding to around 10 images per month in the pseudo-annual time-series approach (Figure 6). In practice, the amount of images per month in the pseudo-annual stack varies between 3 and 16, meaning that the archive data is not suitable for annual or regular monitoring purposes. Figure 9A illustrates the country coverage mosaic of three temporal features: the relative minimum, the relative maximum, and the

The three temporal features, illustrated as a mosaic in Figure 9A, are exclusively carried out for visualization purposes. Indeed, the applied gradient algorithm, which is used to produce a seamless image, may significantly change the radiometric content in the overlapping areas; hence, it is not suitable for data analysis purposes. Looking at the temporal features at country level (Figure 9A), three main colours can be identified: blue, yellow, and light and dark orange to brown. The blue colour represents the minimum, i.e., a constant low radar backscatter during the whole year. Yellow, identified as rice and primarily located in the northeast of Bangladesh, is the combination of a strong temporal variation (red) and a high maximum (green). Light and dark orange to brown, distributed over the whole country, classified as rice, is essentially a combination of a strong temporal variation (red) with a medium maximum

the crop occurs in this period (from January to December).

**4.1. Multi-year rice extent from the ENVISAT WS archive**

under analysis.

134 Land Applications of Radar Remote Sensing

**4. Results**

maximum variation.

(green).

**Figure 9.** (A) Mosaic (100 m) of temporal features based on ENVISAT ASAR Wide Swath data acquired from 2002 to 2010. Red is the maximum variation, green is the relative maximum, and blue is the relative minimum. (B) Rice extent based on temporal features of archive ENVISAT ASAR Wide Swath data. Light green is single rice, dark green is double rice, aquamarine is rice grown after a long flooding duration (up to six months), and orange is rice mixed with other crops. (C) Enlargement of A (red box). (D) Temporal features (15 m) based on ENVISAT ASAR Alternating Polarization data acquired from June 2011 to March 2012 on the same area shown in C. Red is the maximum variation, green is the relative maximum, and blue is the relative minimum.

Due to the different acquisition times and geometry between the two orbits, the five temporal features – derived from the monthly averaged backscattering coefficient – are computed for each orbit separately. The rice extent is generated according to the rules described in section 3.1 and, subsequently, products belonging to the two orbits are shown as a mosaic at semantic level (Figure 9B). In this specific case, rice extent additionally includes the various rice crop seasons, which could be identified with this data set because of the good temporal frequency. Further distinction between single rice, double rice, and rice cultivated after prolonged flooding is provided by the time component—the single rice crop has a short to medium (90-120 days) duration; double rice usually means two short duration crops; and boro rice, after prolonged flooding in the northeast, is usually medium duration. This is a coarse characterisation of rice seasons, due to the fact that temporal features, derived from monthly averaged backscattering coefficient acquired irregularly, are not sufficient to follow the rapid spatial-temporal changes of agriculture. Nevertheless, this pseudo-annual data set enables the generation of a detailed rice–non-rice extent at a one-hectare scale.

With regard to the consistency of the proposed approach and to the impact of spatial resolution, temporal features derived from ASAR Wide Swath (Figure 9C) are compared to those derived from ASAR Alternating Polarization data (Figure 9D). This 15-m resolution data set has been acquired from June 2011 to March 2012 every 35 days, according to the repeat cycle of ENVISAT ASAR. The visual comparison shows that the land cover patterns, particularly of rice, are similar. It means that the multi-year (or pseudo-annual) approach is conceptually correct and viable in this case.

Another interesting observation can be made by comparing the two colour composites spatial resolution plays an important role. Large and homogeneous regions are clearly identifiable in both acquisition modes, while small features tend to be smoothed out and to disappear. Rice covers a substantial part of the country, and although plots are small, the rice areas in much of the country are large and connected, especially in the rainy season, where rice is often the only viable crop. From analysis of SAR data at a range of resolutions (3 to 100 m), we estimate that an accurate area estimation using this approach would need a pixel size at least five times smaller than the field size. Medium and lower spatial resolution systems should be therefore exclusively used for the provision either of extent information — a proxy of area — or, in case of agricultural applications, of phenological crop monitoring.

The ASAR Alternating Polarization HH/HV data were used to further compare the pseudoannual ASAR Wide Swath data to the quasi-annual HH/HV data, particularly with respect to the contribution of the cross-polarization for rice detection. Figure 10 (left) illustrates the temporal signatures of the ASAR WS Swath monthly averaged HH backscattering coefficient from January to December and those at HH and HV acquired in Alternating Polarization mode. First, this reaffirms that the use of multi-temporal data is essential, particularly at HH polari‐ zation. Second, the correspondence between the two HH radar backscatter signatures is evident, confirming the validity of the proposed approach. Third, and confirming the studies of [12,16], the HV polarization contributes in the discrimination of rice from other land covers. Typically, the HH/VV ratio is preferred because it shows a variation up to 7 dB from the beginning of the season to the plant maturity phase [11].

Finally, the quasi-annual HH/HV time-series is used to determine two key moments of the rice growth — start of season and peak of season (Figure 10, right). The first happens when the relative minimum is detected (September, blue colour), whereas the second is detected when the relative maximum is reached (November, green colour). A comparison with the generic Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for… http://dx.doi.org/10.5772/57443 137

**Figure 10.** Top left: Temporal signatures derived from ENVISAT ASAR Wide Swath HH data acquired from 2002 to 2010. Middle left: Temporal signatures derived from ENVISAT ASAR Alternating Polarization HH data acquired from June 2011 to March 2012. Bottom left: Temporal signatures derived from ENVISAT ASAR Alternating Polarization HV data acquired from June 2011 to March 2012. Right: Detected start of season and peak of season based on ENVISAT ASAR Alternating Polarization HH data acquired from June 2011 to March 2012.

crop calendar of Figure 6 shows a good correspondence with the identified dates. However, it should be noted that a 35-day repeat cycle is not sufficient to obtain an accurate estimate of these two key moments. For this reason, the use of remote-sensing data acquired with higher temporal frequency is of advantage.

#### **4.2. 2011 seasonal rice monitoring with MODIS**

#### *4.2.1. Rice season map*

level (Figure 9B). In this specific case, rice extent additionally includes the various rice crop seasons, which could be identified with this data set because of the good temporal frequency. Further distinction between single rice, double rice, and rice cultivated after prolonged flooding is provided by the time component—the single rice crop has a short to medium (90-120 days) duration; double rice usually means two short duration crops; and boro rice, after prolonged flooding in the northeast, is usually medium duration. This is a coarse characterisation of rice seasons, due to the fact that temporal features, derived from monthly averaged backscattering coefficient acquired irregularly, are not sufficient to follow the rapid spatial-temporal changes of agriculture. Nevertheless, this pseudo-annual data set enables the

With regard to the consistency of the proposed approach and to the impact of spatial resolution, temporal features derived from ASAR Wide Swath (Figure 9C) are compared to those derived from ASAR Alternating Polarization data (Figure 9D). This 15-m resolution data set has been acquired from June 2011 to March 2012 every 35 days, according to the repeat cycle of ENVISAT ASAR. The visual comparison shows that the land cover patterns, particularly of rice, are similar. It means that the multi-year (or pseudo-annual) approach is conceptually correct and

Another interesting observation can be made by comparing the two colour composites spatial resolution plays an important role. Large and homogeneous regions are clearly identifiable in both acquisition modes, while small features tend to be smoothed out and to disappear. Rice covers a substantial part of the country, and although plots are small, the rice areas in much of the country are large and connected, especially in the rainy season, where rice is often the only viable crop. From analysis of SAR data at a range of resolutions (3 to 100 m), we estimate that an accurate area estimation using this approach would need a pixel size at least five times smaller than the field size. Medium and lower spatial resolution systems should be therefore exclusively used for the provision either of extent information — a proxy

of area — or, in case of agricultural applications, of phenological crop monitoring.

beginning of the season to the plant maturity phase [11].

The ASAR Alternating Polarization HH/HV data were used to further compare the pseudoannual ASAR Wide Swath data to the quasi-annual HH/HV data, particularly with respect to the contribution of the cross-polarization for rice detection. Figure 10 (left) illustrates the temporal signatures of the ASAR WS Swath monthly averaged HH backscattering coefficient from January to December and those at HH and HV acquired in Alternating Polarization mode. First, this reaffirms that the use of multi-temporal data is essential, particularly at HH polari‐ zation. Second, the correspondence between the two HH radar backscatter signatures is evident, confirming the validity of the proposed approach. Third, and confirming the studies of [12,16], the HV polarization contributes in the discrimination of rice from other land covers. Typically, the HH/VV ratio is preferred because it shows a variation up to 7 dB from the

Finally, the quasi-annual HH/HV time-series is used to determine two key moments of the rice growth — start of season and peak of season (Figure 10, right). The first happens when the relative minimum is detected (September, blue colour), whereas the second is detected when the relative maximum is reached (November, green colour). A comparison with the generic

generation of a detailed rice–non-rice extent at a one-hectare scale.

viable in this case.

136 Land Applications of Radar Remote Sensing

The number of rice seasons detected is reported in Figure 11A. For the year 2011, the analysis of optical data time-series revealed that most areas have one rice season. Two seasons were identified in the northern regions, while up to three seasons (red area in Figure 11A) were found in the northwest part of the country in Rajshahi Division. Areas with one or two rice seasons per year may cultivate a second or third non-rice crop, but those non-rice cropping seasons are not captured in the PhenoRice algorithm, nor is it represented in the map.

The same analysis also produces rice extent maps for the boro, aus, and aman seasons. The main rice cultivation (~% 49) occurs in the boro period (Figure 11B), followed by aman (Figure 11D) during the wet season (~% 45). The aus crop is very small (6%) in comparison to the other two, and occurs mainly in the central and western part of the country (Figure 11C).

**Figure 11.** Total detected rice seasons (A) and cultivated area for boro (B), aus (C) and aman (D) seasons.

Figure 12 reports some examples, extracted from single pixels, of temporal analysis performed by PhenoRice. The panels provide examples for single rice in boro and aman (A, B), double rice on both boro and aman (C), and triple rice in all three seasons (D). As previously described, the algorithm analyses the temporal signature of three spectral indices — EVI (in green), LSWI (blue), and NDWI (light blue)—for vegetation and moisture/water. The figures also report the noise level for each composite data, mainly because of cloud contamination and are repre‐ sented with grey dots, and the occurrence of the detected phenological stages. The symbols in red (square, triangle, diamond, and circle) represent agronomic flooding (MIN – crop trans‐ planting/seeding),; the plants' emergence (SoS-start of the vegetative period of rapid growth); the crop heading/flowering (MAX – start of the reproductive period); and finally, the estimated crop maturity after senescence (EoS).

Figure 12A (pixel, 24°17'54.00"N – 90°59'48.46''E in the Sylhet basin in northeastern Bangla‐ desh) shows the presence of a single rice crop in the boro season; the peculiarity of this location is (1) the strong presence of surface water for nearly six months and (2) a medium crop vegetative phase of about 120 days (period between EoS and SoS occurrence). The identifica‐ tion of these characteristics leads to the assumption that cultivation occurs after prolonged flooding. This observation perfectly matches the ones from the ASAR map and represents an area of low cropping intensity because of excess surface water.

In particular, Figure 12B (pixel, 22°1'30.00"N – 90°6'50.37''E in Barisal Division in coastal Bangladesh) reveals a single long-duration (150 days) rice crop in the rainfed aman season

Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for… http://dx.doi.org/10.5772/57443 139

identified in the northern regions, while up to three seasons (red area in Figure 11A) were found in the northwest part of the country in Rajshahi Division. Areas with one or two rice seasons per year may cultivate a second or third non-rice crop, but those non-rice cropping seasons are not captured in the PhenoRice algorithm, nor is it represented in the map.

The same analysis also produces rice extent maps for the boro, aus, and aman seasons. The main rice cultivation (~% 49) occurs in the boro period (Figure 11B), followed by aman (Figure 11D) during the wet season (~% 45). The aus crop is very small (6%) in comparison to the other

two, and occurs mainly in the central and western part of the country (Figure 11C).

**Figure 11.** Total detected rice seasons (A) and cultivated area for boro (B), aus (C) and aman (D) seasons.

crop maturity after senescence (EoS).

138 Land Applications of Radar Remote Sensing

area of low cropping intensity because of excess surface water.

Figure 12 reports some examples, extracted from single pixels, of temporal analysis performed by PhenoRice. The panels provide examples for single rice in boro and aman (A, B), double rice on both boro and aman (C), and triple rice in all three seasons (D). As previously described, the algorithm analyses the temporal signature of three spectral indices — EVI (in green), LSWI (blue), and NDWI (light blue)—for vegetation and moisture/water. The figures also report the noise level for each composite data, mainly because of cloud contamination and are repre‐ sented with grey dots, and the occurrence of the detected phenological stages. The symbols in red (square, triangle, diamond, and circle) represent agronomic flooding (MIN – crop trans‐ planting/seeding),; the plants' emergence (SoS-start of the vegetative period of rapid growth); the crop heading/flowering (MAX – start of the reproductive period); and finally, the estimated

Figure 12A (pixel, 24°17'54.00"N – 90°59'48.46''E in the Sylhet basin in northeastern Bangla‐ desh) shows the presence of a single rice crop in the boro season; the peculiarity of this location is (1) the strong presence of surface water for nearly six months and (2) a medium crop vegetative phase of about 120 days (period between EoS and SoS occurrence). The identifica‐ tion of these characteristics leads to the assumption that cultivation occurs after prolonged flooding. This observation perfectly matches the ones from the ASAR map and represents an

In particular, Figure 12B (pixel, 22°1'30.00"N – 90°6'50.37''E in Barisal Division in coastal Bangladesh) reveals a single long-duration (150 days) rice crop in the rainfed aman season

**Figure 12.** Example of the temporal dynamics of the vegetation indices — EVI (green line), LSWI+0.05 (blue line), and NDWI (light-blue line), and phenological detection for MIN, SoS, MAX, and EoS. Panels A, B, C, and D show a single season for boro rice, a single season for aman rice, a double season (boro-aman), and a triple season (boro-aus-aman), respectively.

with (1) crop heading (MAX) occurring in November in the fourth quarter of the analysed season, (2) a transplanting period (MIN) in June, (3) a green-up (SoS) in July, and (4) a crop maturity (EoS) in December. This area is highly saline in the dry boro season and no cultivation is possible so the land is left fallow for much of the year, until freshwater availability from rivers or canals increase after the monsoon rains. Thus, this is another example of environ‐ mental conditions limiting cropping intensity. The agronomic flooding from May to August that precedes the crop greening is particularly clear from the interpretation of LWSI and NDWI indices trends. The former, as described by Xiao et al. (2005), shows values far greater than EVI, whereas the latter presents values greater than zero.

Figure 12C (pixel, 25°10'60.00"N – 89°14'42.64''E in Ranjpur Division, northwestern Bangla‐ desh) shows the detection of two rice crops in 2011. The first boro crop occurs during the first and second quarters, from February to June, with crop heading in late March; whereas, the second one, aman rice, takes place in the third and fourth quarters, starting in July and finishing in late October. The boro crop is a short-duration one, 90 to 100 days, whereas the aman rainfed crop is much longer, 130-150 days.

Finally, Figure 12D (pixel 24°36'45.00"N – 89°24'6.25''E, Rajshahi Division in central Bangla‐ desh) illustrates the case of three rice seasons, heading occurrences are in February (boro), July (aus), and October (aman), respectively. In this case, it is possible to appreciate that the crop durations are much shorter than the ones in the previous examples — they are all 90 to 110 days — and could represent short-duration, high-yielding varieties that have done much to increase rice productivity in Bangladesh since the Green Revolution.

### *4.2.2. Phenological detection maps*

Figure 13 shows the phenological detection results for the year 2011. The data represent a spatial analysis conducted at sub-district or *thana* level, taking into account the median value of the different phenological data occurring within each sub-district. Pixel-level interpretation and visualisation is challenging because of the inability of the algorithm to detect every occurrence, the well-known low-resolution bias of MODIS data when observing fields, and the heterogeneity of the cropping systems [27]. Thus, given that PhenoRice is a very conser‐ vative approach to detection (in order to minimize false positive error) and, if we accept that PhenoRice can only capture a proportion of the phenomena understudy, this aggregation of data around a median value gives a more robust interpretation of the phenology data for a selected unit of analysis. Furthermore, we excluded sub-districts if there were fewer than 30 pixels detected for a given season.

The boro maps confirm the well-known patterns of agronomic practices. Fields start to be flooded (MIN boro map) in the southeast, red (Jan 2011), and then the season progresses northwest, orange (Feb 2011). The SoS boro map highlights this trend more clearly. On the other hand, the aman season starts in the north and heads south due to the north-south progression of freshwater in the river systems as depicted by the light green (Jun 2011) and green (Jul-Aug 2011) colours in the MIN map, respectively.

During the boro season, no rice crop is detected in the southwest since that corresponds to the dry season, where there is insufficient freshwater for irrigation and the region is exposed to high water salinity. Freshwater availability increases in the aman season in the southwest during that time. The correct detection of these two seasons in this part of the country suggests that this approach is well suited to the detection of seasonality.

The analysis of the aus map is more difficult because of the smaller and more fragmented area of the rice crop in this season. Aus rice detection is concentrated mainly in the central part of Bangladesh, along the Ganges River, and on the eastern region of Chittagong. The aus crop relies on both early-season irrigation and late-season rainfall, and this partially explains the limited extent. Two cropping patterns could appear: the first one, much earlier in the middle of the country, and the second, a later pattern for the rest of the country.

### **5. Discussion**

### **5.1. Observations on the SAR analysis and results**

Temporal analysis of moderate-resolution SAR, especially using the backscattering coeffi‐ cient (σ°) from C-band time-series, is ideally suited for irrigated and lowland rice detection. It is particularly advantageous in monsoon Asia where much of the world's rice is produced over huge areas and under cloudy conditions.

The use of the 10-year ENVISAT ASAR Wide Swath data archive, even if not optimal for the targeted application – since it is irregularly acquired in temporal terms – provides a valuable

Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for… http://dx.doi.org/10.5772/57443 141

*4.2.2. Phenological detection maps*

140 Land Applications of Radar Remote Sensing

pixels detected for a given season.

**5. Discussion**

green (Jul-Aug 2011) colours in the MIN map, respectively.

that this approach is well suited to the detection of seasonality.

**5.1. Observations on the SAR analysis and results**

huge areas and under cloudy conditions.

of the country, and the second, a later pattern for the rest of the country.

Figure 13 shows the phenological detection results for the year 2011. The data represent a spatial analysis conducted at sub-district or *thana* level, taking into account the median value of the different phenological data occurring within each sub-district. Pixel-level interpretation and visualisation is challenging because of the inability of the algorithm to detect every occurrence, the well-known low-resolution bias of MODIS data when observing fields, and the heterogeneity of the cropping systems [27]. Thus, given that PhenoRice is a very conser‐ vative approach to detection (in order to minimize false positive error) and, if we accept that PhenoRice can only capture a proportion of the phenomena understudy, this aggregation of data around a median value gives a more robust interpretation of the phenology data for a selected unit of analysis. Furthermore, we excluded sub-districts if there were fewer than 30

The boro maps confirm the well-known patterns of agronomic practices. Fields start to be flooded (MIN boro map) in the southeast, red (Jan 2011), and then the season progresses northwest, orange (Feb 2011). The SoS boro map highlights this trend more clearly. On the other hand, the aman season starts in the north and heads south due to the north-south progression of freshwater in the river systems as depicted by the light green (Jun 2011) and

During the boro season, no rice crop is detected in the southwest since that corresponds to the dry season, where there is insufficient freshwater for irrigation and the region is exposed to high water salinity. Freshwater availability increases in the aman season in the southwest during that time. The correct detection of these two seasons in this part of the country suggests

The analysis of the aus map is more difficult because of the smaller and more fragmented area of the rice crop in this season. Aus rice detection is concentrated mainly in the central part of Bangladesh, along the Ganges River, and on the eastern region of Chittagong. The aus crop relies on both early-season irrigation and late-season rainfall, and this partially explains the limited extent. Two cropping patterns could appear: the first one, much earlier in the middle

Temporal analysis of moderate-resolution SAR, especially using the backscattering coeffi‐ cient (σ°) from C-band time-series, is ideally suited for irrigated and lowland rice detection. It is particularly advantageous in monsoon Asia where much of the world's rice is produced over

The use of the 10-year ENVISAT ASAR Wide Swath data archive, even if not optimal for the targeted application – since it is irregularly acquired in temporal terms – provides a valuable

**Figure 13.** From top to bottom: Min, SoS, Max, and EoS phenological metrics for the boro, aus, and aman rice seasons in Bangladesh, in the analysed time window: June 2010-December 2011.

data source, enabling the generation of a consistent rice extent product, with 1-hectare resolution, nationally. In this respect, it is well-known that SAR data availability is currently problematic, mainly because of the recent failure of the ENVISAT ASAR and ALOS PALSAR-1 systems. Nevertheless, today, there are tens of thousands of archived unexploited SAR images that could be leveraged for baseline mapping to generate rice extent and area. This multi-year approach is applicable to Asia because most rice systems are generally not subject to seasonal rotations (upland rice is one exception, but it accounts for a small portion of the total rice area) and there is little scope for rice area expansion in Asia. This is obviously not feasible for all crops, where rotation mandatory and significant land-use change can occur.

The availability of systematic SAR acquisitions at an appropriate time interval — for instance, bi-monthly or weekly, as planned for in the Sentinel-1A/B mission — would allow the generation of annual and seasonal rice area data nationally. Moreover, high-resolution SAR time-series acquired along the whole season could complement the phenological monitoring provided by optical moderate resolution, particularly with respect to the detection of key phenological stages such as the start and peak of season. This would be facilitated if space agencies, with mandates for future SAR missions, could incorporate systematic background missions according to the geographical areas and applications, instead of building data archives based on sporadic/irregular acquisitions.

Concerning the SAR data processing aspect, it is essential that a multi-temporal data process‐ ing approach is performed, in primis, to enhance the data quality — hence, the level of detail — by significantly reducing the speckle. Future hyper-temporal data stacks acquired from Sentinel-1A/B will have an important role for the provision of high-quality data (i.e., high Equivalent Number of Looks) at the highest level of detail. This will permit a pixel-based approach, which is simpler and less time-consuming than a regional based one.

Due to the nature of the multi-year ENVISAT ASAR Wide Swath data (irregularly acquired in temporal terms), the resulting rice temporal signature is occasionally not optimal or not representative enough for rigorous date-by-date analysis. For this reason, we used a pixel-wise temporal signature analysis using selected representative temporal features (relative mini‐ mum and maximum, and the corresponding difference, minimum and maximum gradient between two subsequent acquisitions). Although this approach discriminated between rice and non-rice areas, the features are not sufficient to properly determine the country-wide extent of various rice seasons (single, double, etc.) and, even less, the rice phenology. There are some exceptional cases with sufficient coverage, where various rice seasons can be identified by detecting the amount of minimum and maximum gradients and related dates. In summary, assuming the availability of dense multi-year, regularly acquired SAR images, an average rice crop calendar could be generated together with the rice extent.

### **5.2. Observations on the MODIS analysis and results**

Temporal analysis of moderate-resolution optical data time-series for detection of rice phenology is possible where there is a good rice extent base map from SAR or other sources, and good prior knowledge of the general cropping systems. Phenological occurrence maps show an agreement with local knowledge on rice cultivation, highlighting different gradients of transplanting dates in the country. Analysis and interpretation of Vegetation Index timeseries without prior knowledge or understanding of the crops, cropping patterns, and crop management is extremely challenging and difficult to interpret in terms of an operational crop monitoring system.

The proposed method is able to identify particular crop management systems such as single-, double-, and triple-cropping patterns. Moreover, a range of seasonalities and of crop durations from 90-to 150-day varieties could be detected. Further experiments in rice/wheat, rice/pulse, rice/shrimp and drought-prone areas should be conducted to assess the capability of detection in other commonly occurring rice cropping systems and environments.

The optical time-series data described the temporal pattern of rice systems across Bangladesh. The SAR data describes where rice is cultivated and the optical data describes when rice is cultivated. This is encouraging considering the aforementioned triple challenges of using moderate-resolution data in a region characterised by pervasive cloud cover, small fields, and complex cropping systems. The conservative nature of the phenological detection with moderate-resolution data suggests that pixel-level interpretation and pixel counting for area estimation should be avoided as they are likely to be unreliable estimates of crop system characteristics. Such estimates can always be improved through fine-tuning, expert classifica‐ tion, and other manual interventions, but these do not lend themselves to operational methods that require rapid and robust estimates that are free of user-bias.

Summarising the crop phenology at high levels of aggregation that match existing data or management units could provide one solution to this underestimation effect. PhenoRice is conservative in its detection criteria and, if we can assume that the detected pixels are repre‐ sentative of the cropping systems, then some statistical representation of the seasonality by sub-district, irrigation scheme, or other management unit would provide useful information. Most cropping calendars (e.g. Figure 2) provide ranges for the start and end of season, and remotely sensed phenolology metrics could be used in the same way to provide robust, yearand season-specific information to assess the onset of a late or early season. The same infor‐ mation is vital for yield modelling, since the phenology data can be used to drive/force crop growth simulation models in order to produce more reliable yield estimates.

#### **5.3. Final comments**

data source, enabling the generation of a consistent rice extent product, with 1-hectare resolution, nationally. In this respect, it is well-known that SAR data availability is currently problematic, mainly because of the recent failure of the ENVISAT ASAR and ALOS PALSAR-1 systems. Nevertheless, today, there are tens of thousands of archived unexploited SAR images that could be leveraged for baseline mapping to generate rice extent and area. This multi-year approach is applicable to Asia because most rice systems are generally not subject to seasonal rotations (upland rice is one exception, but it accounts for a small portion of the total rice area) and there is little scope for rice area expansion in Asia. This is obviously not feasible for all

The availability of systematic SAR acquisitions at an appropriate time interval — for instance, bi-monthly or weekly, as planned for in the Sentinel-1A/B mission — would allow the generation of annual and seasonal rice area data nationally. Moreover, high-resolution SAR time-series acquired along the whole season could complement the phenological monitoring provided by optical moderate resolution, particularly with respect to the detection of key phenological stages such as the start and peak of season. This would be facilitated if space agencies, with mandates for future SAR missions, could incorporate systematic background missions according to the geographical areas and applications, instead of building data

Concerning the SAR data processing aspect, it is essential that a multi-temporal data process‐ ing approach is performed, in primis, to enhance the data quality — hence, the level of detail — by significantly reducing the speckle. Future hyper-temporal data stacks acquired from Sentinel-1A/B will have an important role for the provision of high-quality data (i.e., high Equivalent Number of Looks) at the highest level of detail. This will permit a pixel-based

Due to the nature of the multi-year ENVISAT ASAR Wide Swath data (irregularly acquired in temporal terms), the resulting rice temporal signature is occasionally not optimal or not representative enough for rigorous date-by-date analysis. For this reason, we used a pixel-wise temporal signature analysis using selected representative temporal features (relative mini‐ mum and maximum, and the corresponding difference, minimum and maximum gradient between two subsequent acquisitions). Although this approach discriminated between rice and non-rice areas, the features are not sufficient to properly determine the country-wide extent of various rice seasons (single, double, etc.) and, even less, the rice phenology. There are some exceptional cases with sufficient coverage, where various rice seasons can be identified by detecting the amount of minimum and maximum gradients and related dates. In summary, assuming the availability of dense multi-year, regularly acquired SAR images,

Temporal analysis of moderate-resolution optical data time-series for detection of rice phenology is possible where there is a good rice extent base map from SAR or other sources, and good prior knowledge of the general cropping systems. Phenological occurrence maps show an agreement with local knowledge on rice cultivation, highlighting different gradients

approach, which is simpler and less time-consuming than a regional based one.

an average rice crop calendar could be generated together with the rice extent.

**5.2. Observations on the MODIS analysis and results**

crops, where rotation mandatory and significant land-use change can occur.

archives based on sporadic/irregular acquisitions.

142 Land Applications of Radar Remote Sensing

The current structure of the algorithm does not allow a near–real-time analysis since it needs to obtain all remote-sensing data through to the end of the year to provide complete pheno‐ logical information. This can be overcome by interpreting the vegetation indexes or σ° trends at the end of the time-series, which would be refined as new information is added in near– real-time. Thus, for each phenological parameter, there would be a 'possible detection' counterpart that could be provided rapidly and which would be confirmed as a 'real detection' after several more images are acquired. This approach to crop and crop phenology detection is an analogy to what can be achieved with the advent of the Landsat continuity mission and GMES-ESA Sentinel mission, which will, for the first time, provide the possibility of highresolution SAR and multi-spectral time-series analysis with weekly revisit periods. Moderateresolution analysis will still play a role with Proba V, representing an alternative or a complement, to MODIS data.

In this processing chain, we have demonstrated the joint use of active and passive data as a smart method, based on the strengths of both sensors, to produce rice extent information (from multi-year ASAR analysis) and seasonal monitoring (from MODIS analysis). Crop monitoring systems should rely on a range of remote-sensing information sources in order to obtain the best possible information and smart combinations. The ever-increasing range of information from remote sensing is essential if such systems are to become operational, reliable, and accurate.

### **Acknowledgements**

This work has been undertaken within the framework of the Remote Sensing-Based Informa‐ tion and Insurance for Crops in emerging Economies (RIICE) project financially supported by the Swiss Development Cooperation. It was also funded by the CGIAR Global Rice Science Partnership (GRiSP) programme. We are grateful to the European Space Agency for access to the archive of ENVISAT ASAR data.

### **Author details**

Andrew Nelson1\*, Mirco Boschetti2 , Giacinto Manfron2 , Franceco Holecz3 , Francesco Collivignarelli3 , Luca Gatti3 , Massimo Barbieri3 , Lorena Villano1 , Parvesh Chandna4 and Tri Setiyono1

\*Address all correspondence to<EMAIL_ADDRESS>
1 IRRI, Los Baños, Philippines

2 CNR, Milano, Italy

3 Sarmap, Purasca, Switzerland

4 IRRI, Dhaka, Bangladesh

### **References**

[1] National Food Supply data from the Crops Primary Equivalent table. FAOSTAT. http://faostat.fao.org/site/609/defalt.aspx#ancor. (accessed 01-Nov-2013).

[2] Yearbook of Agricultural Statistics of Bangladesh. Bangladesh Bureau of Statistics. http://bbs.gov.bd/PageWebMenuContent.aspx?MenuKey=314. (accessed 01- Nov-2013).

resolution analysis will still play a role with Proba V, representing an alternative or a

In this processing chain, we have demonstrated the joint use of active and passive data as a smart method, based on the strengths of both sensors, to produce rice extent information (from multi-year ASAR analysis) and seasonal monitoring (from MODIS analysis). Crop monitoring systems should rely on a range of remote-sensing information sources in order to obtain the best possible information and smart combinations. The ever-increasing range of information from remote sensing is essential if such systems are to become operational, reliable, and

This work has been undertaken within the framework of the Remote Sensing-Based Informa‐ tion and Insurance for Crops in emerging Economies (RIICE) project financially supported by the Swiss Development Cooperation. It was also funded by the CGIAR Global Rice Science Partnership (GRiSP) programme. We are grateful to the European Space Agency for access to

, Giacinto Manfron2

, Massimo Barbieri3

[1] National Food Supply data from the Crops Primary Equivalent table. FAOSTAT.

http://faostat.fao.org/site/609/defalt.aspx#ancor. (accessed 01-Nov-2013).

, Luca Gatti3

and Tri Setiyono1

\*Address all correspondence to<EMAIL_ADDRESS>
, Franceco Holecz3

, Lorena Villano1

,

,

complement, to MODIS data.

144 Land Applications of Radar Remote Sensing

**Acknowledgements**

**Author details**

Parvesh Chandna4

2 CNR, Milano, Italy

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[14] Bouvet A., Le Toan T., Lam-Dao N. Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data. IEEE Transactions of

[15] Oh Y., Kim Y., Hong JY., Kim YH. Polarimetric Backscattering Coefficients of Floode‐ dRice Fields at L-and C-Bands: Measurements,Modeling, and Data Analysis.IEEE

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**Provisional chapter**

### **Change Detection and Classification Using High Resolution SAR Interferometry Change Detection and Classification Using High Resolution SAR Interferometry**

Azzedine Bouaraba, Nada Milisavljević, Marc Acheroy and Damien Closson Azzedine Bouaraba, Nada Milisavljevic, Marc Acheroy, Damien Closson ´ Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57246

### **1. Introduction**

10.5772/57246

Change detection with Synthetic Aperture Radar (SAR) images involves often a pair of co-registered images acquired over the same area at different times [1]. To identify changes, different methods are commonly applied. These methods differ with respect to the parameters that are used to indicate changes. Since SAR data contain amplitude and phase information, both parameters can be used as change indicators [2, 3].

Incoherent change detection identifies changes by comparing sample estimates of the mean backscatter power taken from the repeat pass image pair [4, 5]. The Coherent Change Detection (CCD) technique uses the coherence of a SAR image pair to quantify changes in the observed amplitude and phase of the image pixels. As the Interferometric SAR (InSAR) phase is sensitive to changes in the spatial distribution of scatterers within a resolution cell, the CCD technique that generally detects the low-coherence areas as ground changes [6, 7] has the potential to detect centimeter changes that may remain undetected using only SAR intensity images (incoherent detection) [4, 8].

With the recent incoming of satellite constellations, delivering high-resolution SAR images, it becomes possible to detect surface changes with fine spatial details and with a short revisiting time. This aspect makes the CCD technique ideal for use in military and scientific applications such as border security and environmental monitoring. All these reasons lead to the development of new methods that enhance the change detection performance [4, 8, 9]. However, two main difficulties must be overcome in order to improve the analysis of CCD results.

The first difficulty concerns the InSAR coherence misestimation. Indeed, the sample coherence estimator is biased, especially for low-coherence values [7, 10]. In addition to the presence of speckle in SAR data [11], the consequence of this bias is the appearance of highly coherent pixels inside the changed areas. Within this context, the change detection performance degrades considerably which complicates more the CCD map interpretation particularly when using high resolution SAR data. Medium resolution SAR images obtained

distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

©2012 Bouaraba, Milisavljevic, Acheroy and Closson, licensee InTech. This is an open access chapter ´

from satellites such as ERS, Envisat and Alos involve the complex multilooking operation to square the pixels. This operation leads to the improvement of the coherence estimation but at the expense of the spatial resolution. Multilooking is not necessary when using high resolution SAR data such as Cosmo-SkyMed (CSK) and TerraSAR-X since the image pixels are already nearly square. Nevertheless, the speckle is more noticeable in high resolution data and the coherence bias is more important.

In [7] a method is proposed to reduce the bias, based on space-averaging of coherence samples over a local area. This method allows for the enhancement of the probability of detecting changes, but a large window size is still needed to detect the entire changes. The Local Fringe Frequencies (LFF), which is a measure of the interferometric phase variability, is successfully applied in [8] as an additional change indicator to clean the aberrant highly coherent pixels inside changed areas. The use of the LFF to enhance the coherence map permits an important improvement in change detection performance and offers the advantage of preserving the spatial resolution.

The second difficulty of the CCD technique concerns the change identification. As the coherence is affected by several factors (such as baseline decorrelation and volume decorrelation), the coherence map can reveal changes that are not only due to man-made activities [1]. An area of low-backscatter strength (e.g., water surface, smooth surface, shadows) leads to decorrelation in the coherence image which is not truly the change of interest. The identification of the changes remains difficult and further investigation is needed.

In this chapter, a classification scheme is proposed with eight classes and the significance of these classes. The change classification is based on the improved coherence map combined with the pair of SAR intensity images to identify the types of change (man-made activity, natural surface decorrelation,...etc.). The two SAR intensity images are not used to detect changes as in [5], but only to support the coherence map analysis and interpretation. A set of three high resolution CSK SAR images is used, which concerns Goma airport in the Democratic Republic of Congo (DRC). High-resolution visible images are also used in the visual qualitative validation process. The results show that the proposed classification scheme is simple and effective for the change detection and identification, and that it contributes significantly to the overall scene analysis and understanding.

### **2. Methods**

### **2.1. SAR Interferometry**

InSAR is a technique that exploits the phase differences of at least two SAR images acquired from different orbit positions and/or at different times [3]. In Repeat-pass interferometry the SAR system flies on (ideally) parallel tracks and views the terrain from slightly different directions at different times. Fig. 1 shows a typical spaceborne InSAR configuration. SAR 1 and SAR 2 denote the satellite positions when the first and second SAR images were taken. The distance between them is called the (geometric) baseline and is denoted as *B* in this figure. The perpendicular baseline, *B*⊥, is the component of *B* in the direction perpendicular to the SAR 2 look direction. *R*<sup>1</sup> and *R*<sup>2</sup> denote the ranges to the target *T* from satellite positions SAR 1 and SAR 2 respectively. The principle of the SAR Interferometry technique

**Figure 1.** Interferometric configuration. Satellite positions SAR 1 and SAR 2 have ranges, to target *T*, *R*<sup>1</sup> and *R*<sup>2</sup> respectively. The separation *B* between the satellites is called the spatial baseline.

is the use of the phase information of every pixel which is directly related to the parallaxe <sup>∆</sup>*<sup>R</sup>* = *<sup>R</sup>*<sup>2</sup> − *<sup>R</sup>*1. Let

$$f(\mathbf{R}, \mathbf{x}) = |f(\mathbf{R}, \mathbf{x})| \, \exp\{j\phi\_1(\mathbf{R}, \mathbf{x})\} \text{ and } \ g(\mathbf{R}, \mathbf{x}) = |\mathbf{g}(\mathbf{R}, \mathbf{x})| \, \exp\{j\phi\_2(\mathbf{R}, \mathbf{x})\} \tag{1}$$

be the two co-registered SAR images forming the interferogram

$$I(\cdot) = f(\cdot)g^\*(\cdot) \tag{2}$$

the phase of which

2 Land Applications of Radar Remote Sensing

data and the coherence bias is more important.

advantage of preserving the spatial resolution.

needed.

**2. Methods**

**2.1. SAR Interferometry**

from satellites such as ERS, Envisat and Alos involve the complex multilooking operation to square the pixels. This operation leads to the improvement of the coherence estimation but at the expense of the spatial resolution. Multilooking is not necessary when using high resolution SAR data such as Cosmo-SkyMed (CSK) and TerraSAR-X since the image pixels are already nearly square. Nevertheless, the speckle is more noticeable in high resolution

In [7] a method is proposed to reduce the bias, based on space-averaging of coherence samples over a local area. This method allows for the enhancement of the probability of detecting changes, but a large window size is still needed to detect the entire changes. The Local Fringe Frequencies (LFF), which is a measure of the interferometric phase variability, is successfully applied in [8] as an additional change indicator to clean the aberrant highly coherent pixels inside changed areas. The use of the LFF to enhance the coherence map permits an important improvement in change detection performance and offers the

The second difficulty of the CCD technique concerns the change identification. As the coherence is affected by several factors (such as baseline decorrelation and volume decorrelation), the coherence map can reveal changes that are not only due to man-made activities [1]. An area of low-backscatter strength (e.g., water surface, smooth surface, shadows) leads to decorrelation in the coherence image which is not truly the change of interest. The identification of the changes remains difficult and further investigation is

In this chapter, a classification scheme is proposed with eight classes and the significance of these classes. The change classification is based on the improved coherence map combined with the pair of SAR intensity images to identify the types of change (man-made activity, natural surface decorrelation,...etc.). The two SAR intensity images are not used to detect changes as in [5], but only to support the coherence map analysis and interpretation. A set of three high resolution CSK SAR images is used, which concerns Goma airport in the Democratic Republic of Congo (DRC). High-resolution visible images are also used in the visual qualitative validation process. The results show that the proposed classification scheme is simple and effective for the change detection and identification, and that it

InSAR is a technique that exploits the phase differences of at least two SAR images acquired from different orbit positions and/or at different times [3]. In Repeat-pass interferometry the SAR system flies on (ideally) parallel tracks and views the terrain from slightly different directions at different times. Fig. 1 shows a typical spaceborne InSAR configuration. SAR 1 and SAR 2 denote the satellite positions when the first and second SAR images were taken. The distance between them is called the (geometric) baseline and is denoted as *B* in this figure. The perpendicular baseline, *B*⊥, is the component of *B* in the direction perpendicular to the SAR 2 look direction. *R*<sup>1</sup> and *R*<sup>2</sup> denote the ranges to the target *T* from satellite positions SAR 1 and SAR 2 respectively. The principle of the SAR Interferometry technique

contributes significantly to the overall scene analysis and understanding.

$$
\phi(\cdot) = \phi\_1(\cdot) - \phi\_2(\cdot) \tag{3}
$$

is the interferometric phase.

The phase of the SAR image response *φ* of a point scatterer is proportional to range (where *k* is the factor of proportionality) plus a possible shift *φ*scat due to the scatterer itself [10], i.e.

$$
\phi\_1 = -2kR\_1 + \phi\_{\text{scat, }1} \tag{4}
$$

$$
\phi\_2 = -2kR\_2 + \phi\_{\text{scat, 2}} \tag{5}
$$

Assuming that there is no possible ground displacement of the scatterers between the observations, the interferometric phase contains the following terms [3]:

$$
\phi = \phi\_{topo} + \Delta\phi\_{scat} \tag{6}
$$

where:

• *<sup>φ</sup>topo* <sup>∼</sup><sup>=</sup> <sup>4</sup>*<sup>π</sup> λ B*⊥ *<sup>R</sup>* sin *<sup>θ</sup> z* is the topographic term; • ∆*φscat* represents the influence of any change in scattering behavior. It may be a deterministic phase offset (i.e. change in dielectric constant) or a random phase (i.e. temporal decorrelation).

For Digital Terrain Model extraction, the first term *φtopo* is improved when the other is minimized. In CCD applications, the term ∆*φscat* is of interest and its variability is measured by the coherence defined in the next section.

#### **2.2. InSAR coherence**

#### *2.2.1. Coherence estimation*

In order to provide some measure of discrimination in the SAR image pair and to accommodate the random noise fluctuations, the InSAR coherence is commonly used. The sample coherence, which is defined as the magnitude of the estimated sample complex cross-correlation coefficient between the SAR image pair, encodes the degree of scene similarity as a value in the range [0, 1][6, 7]:

$$\gamma\_N e^{j\phi'} = \frac{\sum\_{i=1}^N f\_i \mathbf{g}\_i^\*}{\sqrt{\sum\_{i=1}^N |f\_i|^2 \sum\_{i=1}^N |g\_i|^2}} \tag{7}$$

where *<sup>γ</sup><sup>N</sup>* is the sample coherence obtained by *<sup>N</sup>* measurements, and *<sup>φ</sup>*′ is the filtered interferometric phase.

As shown in [1], the coherence is affected by different contributions which are mainly related to:

In order to assess the suitability of using the InSAR coherence to detect man-made changes, some important results of interferometric SAR processing are presented in the following. Fig. 2 shows CSK intensity images and their coherence image in two different environment types. The white pixels of the intensity images indicate objects that reflect strong energy toward the satellite antenna, whereas the dark pixels indicate surfaces that do not reflect energy toward the satellite. In the coherence image, white pixels represent values of coherence near 1, while dark pixels represent values near 0.

In the port area, one can clearly distinguish the sea that is indicated by Fig. 2(a)-1, the dock (quay) in Fig. 2(a)-2, and the vehicles terminal in Fig. 2(a)-3. In the coherence image of Fig. 2(c), the sea area is characterized by low coherence values as it concerns incoherent medium. The coherence of the quay is preserved, as indicated in Fig. 2(c)-2, because of the absence of disturbance in the surface roughness. For the vehicles terminal, the coherence is

4 Land Applications of Radar Remote Sensing

temporal decorrelation).

**2.2. InSAR coherence**

*2.2.1. Coherence estimation*

interferometric phase.

image pair,

to:

by the coherence defined in the next section.

similarity as a value in the range [0, 1][6, 7]:

*<sup>γ</sup>Ne<sup>j</sup>φ*′

factor in the repeat pass SAR interferometry [4].

coherence near 1, while dark pixels represent values near 0.

• ∆*φscat* represents the influence of any change in scattering behavior. It may be a deterministic phase offset (i.e. change in dielectric constant) or a random phase (i.e.

For Digital Terrain Model extraction, the first term *φtopo* is improved when the other is minimized. In CCD applications, the term ∆*φscat* is of interest and its variability is measured

In order to provide some measure of discrimination in the SAR image pair and to accommodate the random noise fluctuations, the InSAR coherence is commonly used. The sample coherence, which is defined as the magnitude of the estimated sample complex cross-correlation coefficient between the SAR image pair, encodes the degree of scene

<sup>=</sup> <sup>∑</sup>*<sup>N</sup>*

∑*N*

where *<sup>γ</sup><sup>N</sup>* is the sample coherence obtained by *<sup>N</sup>* measurements, and *<sup>φ</sup>*′ is the filtered

As shown in [1], the coherence is affected by different contributions which are mainly related

1. the relative backscatter signal to radar receiver noise ratio (*SNR*) in the interferometric

3. the temporal decorrelation caused by changes in the land surface, e.g., man-made objects, vegetation change, ploughing or wind action in desert areas, ... etc. It is the dominant

In order to assess the suitability of using the InSAR coherence to detect man-made changes, some important results of interferometric SAR processing are presented in the following. Fig. 2 shows CSK intensity images and their coherence image in two different environment types. The white pixels of the intensity images indicate objects that reflect strong energy toward the satellite antenna, whereas the dark pixels indicate surfaces that do not reflect energy toward the satellite. In the coherence image, white pixels represent values of

In the port area, one can clearly distinguish the sea that is indicated by Fig. 2(a)-1, the dock (quay) in Fig. 2(a)-2, and the vehicles terminal in Fig. 2(a)-3. In the coherence image of Fig. 2(c), the sea area is characterized by low coherence values as it concerns incoherent medium. The coherence of the quay is preserved, as indicated in Fig. 2(c)-2, because of the absence of disturbance in the surface roughness. For the vehicles terminal, the coherence is

2. the baseline decorrelation that is related to the satellite tracks separation, and

*<sup>i</sup>*=<sup>1</sup> *fig*<sup>∗</sup>

*<sup>i</sup>*=<sup>1</sup> <sup>|</sup> *fi*|<sup>2</sup> <sup>∑</sup>*<sup>N</sup>*

*i*

*<sup>i</sup>*=<sup>1</sup> <sup>|</sup>*gi*|<sup>2</sup>

(7)

**Figure 2.** SAR intensity image pairs and the corresponding coherences. (a) CSK intensity image of June 15, 2011 acquired over a port area. (b) CSK intensity image of June 19, 2011. (c) Coherence image of June 15 and 19, 2011. (d) CSK intensity image of January 1, 2010 acquired over an agricultural area. (e) CSK intensity image of January 9, 2010. (f) Coherence image of January 1 and 9, 2010. Imaged scene size of 300×300 m2.

low in Fig. 2(c)-3, as a result of moved vehicles between the two acquisition dates. The changes related to the man-made objects, such as vehicles and containers, may also be detected via incoherent techniques, which only use SAR intensity images [12]. However, in this situation the coherence can offer the advantage of being able to detect changes even if a vehicle are moved and replaced by another one, which is very similar. This is not possible with incoherent methods.

The use of coherence to measure surface changes is even more interesting for the analysis of examples in agricultural environment. The two intensity images of Fig. 2(d) and Fig. 2(e) are nearly identical showing the limited capability of incoherent methods. With an interferometric SAR processing, the cultivated parcels are clearly indicated by low coherence values (Fig. 2(f)-2), while the uncultivated parcels are characterized by high coherence values (Fig. 2(f)-1). In terms of the interferometric phase, which mainly measures the terrain topography, the obtained values are quite steady in the undisturbed areas, while the values are randomly distributed in the changed areas causing a loss of coherence. In military applications, most of the interesting man-made changes, such as vehicle tracks in a no-man's land, are not indicated by the intensity images but rather by the interferometric phase. This is true in particular for X-band SAR data that are highly sensitive to centimeter changes in the scatterers distribution within resolution cell [4].

**Figure 3.** InSAR coherence histograms of the cultivated and uncultivated areas using different number *N* of samples. (a) Coherence evaluated without complex multilooking. (b) Coherence evaluated with complex multilooking of 4 pixels.

The results in Fig. 2 demonstrate the appropriateness of the SAR coherence, which uses both intensity images and interferometric phase to detect all man-made changes. However, there are two difficulties to overcome. On the one hand, the coherence can indicate some changes as in Fig. 2(f)-3, which are not of interest but only due to low backscattering (*SNR*) in the two SAR images (Fig. 2(d)-3 and Fig. 2(e)-3). This situation, that mainly occurs in presence of smooth surfaces (roofs, roads, shadows,...etc.) constitutes the main raison for what we propose in the present work: to develop a classification scheme that permits identification of man-made changes. On the other hand, there is a problem of coherence misestimation, causing highly coherent pixels inside changed areas which complicates the coherence image interpretation.

#### *2.2.2. Coherence bias*

As demonstrated in [7], the InSAR coherence estimator is biased especially for low coherence values. The bias is due to the limited sample size (*N* measurements) in the numerical computation of the Estimate (7). Fig. 3 depicts histograms of coherence images evaluated for different number of samples *N*. It can be seen that the coherence bias induces the presence of highly coherent pixels inside changed areas (light-colored pixels in Fig. 2(f)-2).

The coherence bias decreases when the number of samples used to estimate the coherence increase. For example, the coherence evaluated using *N* = 5 × 5 represents more faithfully the coherence of the changed area than the coherence evaluated by *N* = 3 × 3, as there are less samples of high coherence inside the changed area. According to Fig. 3-(a), the coherence mean value of the cultivated parcel is 0.24 with *N* = 5 × 5 against 0.43 for *N* = 3 × 3. Note that the coherence histograms correspond to the same area of Fig. 2(f)-2, and that the discrepancy in the results is only due to the increasing number *N* of samples. On the contrary, in case of an undisturbed area as in Fig. 2(f)-1, the coherence mean value is practically not affected by the increase of *N*. According to Fig. 3-(a), the coherence mean value of the unchanged area is 0.82 for both *N* = 3 × 3 and 5 × 5, which may be explained by the fact that the coherence estimate is particularly biased for the low coherence values. Fig. 3-(b) shows that the use of the complex multilooking of the SAR images leads to a better

**Figure 4.** Scheme of the LFF-based CCD method.

6 Land Applications of Radar Remote Sensing

interpretation.

*2.2.2. Coherence bias*

(a) (b)

**Figure 3.** InSAR coherence histograms of the cultivated and uncultivated areas using different number *N* of samples. (a) Coherence evaluated without complex multilooking. (b) Coherence evaluated with complex multilooking of 4 pixels.

The results in Fig. 2 demonstrate the appropriateness of the SAR coherence, which uses both intensity images and interferometric phase to detect all man-made changes. However, there are two difficulties to overcome. On the one hand, the coherence can indicate some changes as in Fig. 2(f)-3, which are not of interest but only due to low backscattering (*SNR*) in the two SAR images (Fig. 2(d)-3 and Fig. 2(e)-3). This situation, that mainly occurs in presence of smooth surfaces (roofs, roads, shadows,...etc.) constitutes the main raison for what we propose in the present work: to develop a classification scheme that permits identification of man-made changes. On the other hand, there is a problem of coherence misestimation, causing highly coherent pixels inside changed areas which complicates the coherence image

As demonstrated in [7], the InSAR coherence estimator is biased especially for low coherence values. The bias is due to the limited sample size (*N* measurements) in the numerical computation of the Estimate (7). Fig. 3 depicts histograms of coherence images evaluated for different number of samples *N*. It can be seen that the coherence bias induces the presence

The coherence bias decreases when the number of samples used to estimate the coherence increase. For example, the coherence evaluated using *N* = 5 × 5 represents more faithfully the coherence of the changed area than the coherence evaluated by *N* = 3 × 3, as there are less samples of high coherence inside the changed area. According to Fig. 3-(a), the coherence mean value of the cultivated parcel is 0.24 with *N* = 5 × 5 against 0.43 for *N* = 3 × 3. Note that the coherence histograms correspond to the same area of Fig. 2(f)-2, and that the discrepancy in the results is only due to the increasing number *N* of samples. On the contrary, in case of an undisturbed area as in Fig. 2(f)-1, the coherence mean value is practically not affected by the increase of *N*. According to Fig. 3-(a), the coherence mean value of the unchanged area is 0.82 for both *N* = 3 × 3 and 5 × 5, which may be explained by the fact that the coherence estimate is particularly biased for the low coherence values. Fig. 3-(b) shows that the use of the complex multilooking of the SAR images leads to a better

of highly coherent pixels inside changed areas (light-colored pixels in Fig. 2(f)-2).

**Figure 5.** LFF histograms of the cultivated and uncultivated parcels of Figure 2(f).

separation between the changed and unchanged areas, but at the expense of the spatial resolution.

The results of Fig. 3, similar to those in [7], show the necessity to increase the number of samples to obtain a good separation between coherence values of the changed and unchanged areas. In CCD applications, the challenge consists in separating as much as possible the changed and the unchanged pixels, using small window-size *N* and without multilooking, to preserve subtle changes in the coherence image as much as possible.

In this work, the SAR interferometric processing is done in full resolution with a small window-size of *N* = 3 × 3 pixels, corresponding approximately to an area of 2.4×2.4 m2. According to Fig. 3, this corresponds to the most unfavorable situation of bad separation between the changed and unchanged classes, thus an additional processing step of coherence bias removal is necessary to improve the coherence map.

#### *2.2.3. Coherence map improvement using LFF*

The basic space averaging operation over *M*-pixel local area is commonly performed for further coherence bias removal [7]:

$$z = \frac{1}{M} \sum\_{i=1}^{M} \gamma\_{Ni} \overset{H\_0}{\underset{H\_1}{\gtrless}} T\_{\mathfrak{C}} \tag{8}$$

where *H*<sup>0</sup> is a realization of the null hypothesis (scene changes of interest absent) and *H*<sup>1</sup> is the alternative hypothesis (scene changes of interest present). To make the decision, the statistic *z* is compared to a detection threshold *Tc* [8].

As shown in [8], a large number of samples *N* and a large window-size *M* are needed to detect the entire set of changes, which is obviously at the expense of the preservation of small changes. For that reason, another information source must be used to better analyze the coherence image. In the following paragraphs, we describe a method for the coherence map improvement, which is based on the exploitation of the LFF information.

Using a 2-D notation, the complex InSAR phase can be modeled by a first-order approximation (for simplicity, noise is neglected) [13]:

$$e^{j\phi(k,l)} = e^{j2\pi(kf\_x+lf\_y)}\tag{9}$$

where *fx* and *fy* are the LFF components in range and azimuth directions respectively.

According to Equation (6) and by assuming that the neighboring pixels have the almost identical terrain height, which is valid only using high resolution images, the change components may also be obtained by directly differentiating the interferometric phase,

$$\begin{cases} \Delta \phi\_{\text{scat}-x} = \phi(k+1,l) - \phi(k,l) \\ \Delta \phi\_{\text{scat}-y} = \phi(k,l+1) - \phi(k,l) \end{cases} \tag{10}$$

According to (9) and (10), the LFF estimates may correspond to the change components that can be obtained by differentiating the interferometric phase directly, a process which is not restricted to the first-order model that is due to the fact that *φ*(*k*, *l*) in (7) includes all the frequencies components (*fxi*, *fyi*) that exist inside the *N*-pixel local area [13]. Therefore, the measured interferometric phase is highly influenced by noise. In this context, the LFF estimation can be obtained via either the Maximum Likelihood (ML) method [14] or the MUltiple SIgnal Classification (MUSIC) method [15].

An analysis of Fig. 5 indicates that the two LFF histograms corresponding to the cultivated and uncultivated parcels of Fig. 2(f) are far apart. This confirms that the LFF components represent an additional indicator of changes. Here, the MUSIC method is used since it provides better LFF histograms separation as shown in Fig. 5. Various SAR image pairs have been used to evaluate LFF histograms for both changed (cultivated parcels, sea area) and unchanged areas. It was observed that the location of the histograms' intersection does not change significantly. As a results, it is possible to set the LFF threshold to *Tl* = 0.1 as shown in Fig. 5, an important aspect for window-size adaptation.

Here, the coherence improvement is achieved using the LFF information as an additional change indicator, to clean the highly coherent pixels inside the changed areas that are also characterized by high LFF values [8].

Fig. 6 depicts InSAR coherence maps obtained with and without the LFF information. The area of interest concerns fields with a dense agricultural activity. The result of a simple thresholding method (without space-averaging, i.e. *M* = 1) of the coherence, see Fig. 6(a), is not usable due to the presence of a large number of highly coherent pixels inside the changed

**Figure 6.** InSAR coherence result using *N* = 3 × 3 samples. (a) Coherence map without space-averaging (*M* = 1). (b) Coherence map with space-averaging (*M* = 3 × 3). (c) Change map using the LFF information. (d) Zoom on a cultivated parcel. White pixels indicate unchanged areas, while dark pixels indicate changed areas.

area. In this case, a probability of detection *Pd* = 0.64 is only achieved, leading to an unusable coherence map. An enhancement of the result is obtained by space-averaging coherence, see Fig. 6(b), using a 3 × 3 window. In this case, the detection probability improves to *Pd* = 0.94 but remains insufficient. The best result is obtained by using the LFF information, as presented in Fig. 6(c), with a detection probability *Pd* = 0.99. In this case, the highly coherent pixels inside changed areas are mostly reduced (detection of almost all changes). The zoom of the changed area, as presented in Fig. 6(d), clearly indicates that the method using the LFF information outperforms the existing coherence space-averaging method.

#### **2.3. Proposed classification scheme**

8 Land Applications of Radar Remote Sensing

statistic *z* is compared to a detection threshold *Tc* [8].

approximation (for simplicity, noise is neglected) [13]:

MUltiple SIgnal Classification (MUSIC) method [15].

in Fig. 5, an important aspect for window-size adaptation.

characterized by high LFF values [8].

where *H*<sup>0</sup> is a realization of the null hypothesis (scene changes of interest absent) and *H*<sup>1</sup> is the alternative hypothesis (scene changes of interest present). To make the decision, the

As shown in [8], a large number of samples *N* and a large window-size *M* are needed to detect the entire set of changes, which is obviously at the expense of the preservation of small changes. For that reason, another information source must be used to better analyze the coherence image. In the following paragraphs, we describe a method for the coherence

Using a 2-D notation, the complex InSAR phase can be modeled by a first-order

where *fx* and *fy* are the LFF components in range and azimuth directions respectively.

According to Equation (6) and by assuming that the neighboring pixels have the almost identical terrain height, which is valid only using high resolution images, the change components may also be obtained by directly differentiating the interferometric phase,

∆*φscat*−*<sup>x</sup>* = *φ*(*k* + 1, *l*) − *φ*(*k*, *l*)

According to (9) and (10), the LFF estimates may correspond to the change components that can be obtained by differentiating the interferometric phase directly, a process which is not restricted to the first-order model that is due to the fact that *φ*(*k*, *l*) in (7) includes all the frequencies components (*fxi*, *fyi*) that exist inside the *N*-pixel local area [13]. Therefore, the measured interferometric phase is highly influenced by noise. In this context, the LFF estimation can be obtained via either the Maximum Likelihood (ML) method [14] or the

An analysis of Fig. 5 indicates that the two LFF histograms corresponding to the cultivated and uncultivated parcels of Fig. 2(f) are far apart. This confirms that the LFF components represent an additional indicator of changes. Here, the MUSIC method is used since it provides better LFF histograms separation as shown in Fig. 5. Various SAR image pairs have been used to evaluate LFF histograms for both changed (cultivated parcels, sea area) and unchanged areas. It was observed that the location of the histograms' intersection does not change significantly. As a results, it is possible to set the LFF threshold to *Tl* = 0.1 as shown

Here, the coherence improvement is achieved using the LFF information as an additional change indicator, to clean the highly coherent pixels inside the changed areas that are also

Fig. 6 depicts InSAR coherence maps obtained with and without the LFF information. The area of interest concerns fields with a dense agricultural activity. The result of a simple thresholding method (without space-averaging, i.e. *M* = 1) of the coherence, see Fig. 6(a), is not usable due to the presence of a large number of highly coherent pixels inside the changed

*ejφ*(*k*,*l*) = *ej*2*π*(*k fx*+*l fy* ) (9)

<sup>∆</sup>*φscat*−*<sup>y</sup>* <sup>=</sup> *<sup>φ</sup>*(*k*, *<sup>l</sup>* <sup>+</sup> <sup>1</sup>) <sup>−</sup> *<sup>φ</sup>*(*k*, *<sup>l</sup>*) (10)

map improvement, which is based on the exploitation of the LFF information.

After the InSAR coherence improvement step, the detected changes must be identified as the coherence is affected by several decorrelation sources. To identify the man-made activities in a CCD map, the improved coherence map was combined with the two corresponding SAR intensity images. Fig. 7 presents a schematic overview of the processing chain used for the change classification.

**Figure 7.** Schematic overview of the change detection and classification.

**Table 1.** Overview of the eight classes resulting from the change detection and classification.

Human activity is thus characterized by a low coherence and a high intensity in at least one of the two SAR images. However, any decision based on SAR intensity is hampered by the presence of speckle which leads to an increase in the number of false alarms. Therefore, a speckle reduction is performed prior to the change classification: a 3 × 3 Lee filter [16] is applied to SAR intensity images in this work. The developed classification scheme is quite simple and is based on a combination of thresholds on the three features, i.e. the enhanced coherence and the two corresponding Lee-filtered intensity images. The coherence map threshold corresponds to the intersection of coherence histograms of the changed (cultivated field) and unchanged areas of Fig. 3 [8].

The intensity threshold is determined from the histograms intersection of the learning sets to sub-divide each of the two SAR images into a low (L) and a high (H) value area. The learning set consists in two classes: low backscattered area (smooth surface or water surface) and

**Figure 8.** Geographical location of the test site in Goma (DRC) that is characterized by a flat topography. The grey rectangle indicates the borders of the imaged scene corresponding to the used CSK SAR images of 14568 × 14376 pixels.

high backscattered area (rough surface). For each of the two classes an area of approximately 200 × 200 pixels is identified in the scene. The actual change detection and characterization is a rule-based set of decisions applied to the thresholded SAR images and to the coherence map.

Dividing each of the three feature sets into two value regions leads to eight possible combinations, thus eight possible classes. Table 1 presents an overview of the properties of these classes. Classes of interest for change detection and activity monitoring are C2, C3 and C4. Classes C1, C5 and C8 contribute to the overall scene understanding. C6 and C7 represent classes in which the coherence is high but the intensity changes between the two images. If this situation occurs, it is most probably due to the fact that the intensity value exceeds the threshold in a region where it should not (the tails of the histograms).

It is also possible to divide the coherence into 3 regions; low (L), medium (M) and high (H). This situation would lead to 12 classes that complicate further the CCD map analysis and interpretation. For this reason a binary thresholding of the coherence had been chosen first, which proves to be sufficient for identification of all man-made changes. In future work, the influence of the intermediate (medium) level on the quality of the final result will be analyzed.

### **3. Experimental results**

10 Land Applications of Radar Remote Sensing

**Figure 7.** Schematic overview of the change detection and classification.

SAR-1 SAR-2

Coherence

field) and unchanged areas of Fig. 3 [8].

Class Features Interpretation

C1 L L L Specular surfaces: water, roads,

C5 H L L Bare soil or low vegetation

**Table 1.** Overview of the eight classes resulting from the change detection and classification.

C2 L L H Man-made objects present in SAR-2, not in SAR-1 C3 L H L Man-made objects present in SAR-1, not in SAR-2 C4 L H H Man-made object present in both images but

C6 H L H Invalid class (problem of intensity thresholding

C7 H H L Invalid class ( problem of intensity thresholding

C8 H H H Scatterers present in both scenes: fixed structures

Human activity is thus characterized by a low coherence and a high intensity in at least one of the two SAR images. However, any decision based on SAR intensity is hampered by the presence of speckle which leads to an increase in the number of false alarms. Therefore, a speckle reduction is performed prior to the change classification: a 3 × 3 Lee filter [16] is applied to SAR intensity images in this work. The developed classification scheme is quite simple and is based on a combination of thresholds on the three features, i.e. the enhanced coherence and the two corresponding Lee-filtered intensity images. The coherence map threshold corresponds to the intersection of coherence histograms of the changed (cultivated

The intensity threshold is determined from the histograms intersection of the learning sets to sub-divide each of the two SAR images into a low (L) and a high (H) value area. The learning set consists in two classes: low backscattered area (smooth surface or water surface) and

roofs, shadows

it changed from SAR-1 to SAR-2

caused by speckle)

caused by speckle)

(e.g. parts of buildings, railways), undisturbed areas

### **3.1. Data and study areas**

The results presented in this work are obtained using the X-band CSK SAR images, horizontally polarized in spot-light mode. The CSK images are acquired on 24 March, 28 March and 1 April 2011, with an incident angle of 26◦. The pixel resolution is 0.73 m in the ground range direction and 0.7 m in the azimuth direction. The test site, shown in Fig. 8, concerns the Goma airport in DRC. For the validation of the classification scheme, high-resolution visible images are used with the SAR intensity images to serve as ground truth. The co-registration of the SAR images was performed with Sarscape software (Sarmap 2012). Programs and algorithms were developed in Interactive Data Language (IDL).

The area of interest, shown in Fig. 9(a), concerns a part of the Goma airport that is a busy and flat test site. The airport was under extension, and an important earth moving activity was recorded during the acquisition period of 24 March to 1 April 2011. The scene concerns a wide open field surrounded by buildings and an urban area. Four zones are identified in the test site of Fig. 9(a). Firstly, the area occupied by the company in charge of the airport extension, as indicated by Fig. 9(a)-1, is surrounded by roads where excavators are clearly visible. The anthropic activities are mainly concentrated in the dike that is indicated by Fig. 9(a)-2 and in an area pointed by Fig. 9(a)-3. The dike causes a distortion (shadow) in the SAR image as indicated in Fig. 9(b)-2. The fourth zone concerns the airport runway that is indicated by Fig. 9(a)-4. It is characterized by a low backscattering power as shown in Fig. 9(b)-4. In the urban area, left side of Fig. 9(b), most of the roofs are characterized by a low backscattering power.

### **3.2. Results and analysis**

Fig. 9(c) shows the InSAR coherence map of the imaged area obtained using the CSK acquisitions of March 24 and 28, 2011. Light-colored pixels represent values of coherence near 1, while dark pixels represent values near 0. Areas with high coherence indicate the absence of surface perturbation. Places with low coherence, corresponding to the areas disturbed between the two acquisitions dates, are located e.g. in Fig. 9(c)-1, 2 and 3. All roads leading to the holding area of Fig. 9(c)-1 have lost the coherence, due to the movements of the excavators.

The analysis and interpretation of the InSAR coherence map become complicated in the presence of man-made structures. Indeed, for example, the big building in Fig. 9(c)-1 is characterized by a low coherence in Fig. 9(c)-1, which is not truly the change that is of interest. The change classification results (or CCD map) corresponding to the period of 24 and 28 March 2011 are depicted in Fig. 9(d). Only the changes C2, C3 and C4 are of interest, and the other classes help to analyze the scene. Note that the incoherent change detection methods can detect the C2 and C3 changes, but fail to detect the C4 changes (type of change that is revealed by the interferometric phase). Besides the advantages of the CCD technique, the classification contributes to the scene analysis. The large building in Fig. 9(d)-1, and most of the urban area on the left side of Fig.9(d), is now classified as C1 instead of change in the coherence map.

The analysis of the results of Fig. 9(c) and Fig. 9(d) shows that in a simple environment without obstacles (i.e., open field in the middle of the scene shown in Fig. 9(a)), the classification method identifies the man-made changes as belonging to classes C2, C3 or C4 while the coherence map confuses the changes in a single category. In a complex environment, e. g. of the urban area in the left part of the scene presented in Fig. 9(a) and the airport runway in Fig. 9(c)-4, the InSAR coherence map becomes hard to interpret and the proposed change classification scheme identifies well the man-made changes and contributes

12 Land Applications of Radar Remote Sensing

low backscattering power.

**3.2. Results and analysis**

of the excavators.

coherence map.

March and 1 April 2011, with an incident angle of 26◦. The pixel resolution is 0.73 m in the ground range direction and 0.7 m in the azimuth direction. The test site, shown in Fig. 8, concerns the Goma airport in DRC. For the validation of the classification scheme, high-resolution visible images are used with the SAR intensity images to serve as ground truth. The co-registration of the SAR images was performed with Sarscape software (Sarmap 2012). Programs and algorithms were developed in Interactive Data Language (IDL).

The area of interest, shown in Fig. 9(a), concerns a part of the Goma airport that is a busy and flat test site. The airport was under extension, and an important earth moving activity was recorded during the acquisition period of 24 March to 1 April 2011. The scene concerns a wide open field surrounded by buildings and an urban area. Four zones are identified in the test site of Fig. 9(a). Firstly, the area occupied by the company in charge of the airport extension, as indicated by Fig. 9(a)-1, is surrounded by roads where excavators are clearly visible. The anthropic activities are mainly concentrated in the dike that is indicated by Fig. 9(a)-2 and in an area pointed by Fig. 9(a)-3. The dike causes a distortion (shadow) in the SAR image as indicated in Fig. 9(b)-2. The fourth zone concerns the airport runway that is indicated by Fig. 9(a)-4. It is characterized by a low backscattering power as shown in Fig. 9(b)-4. In the urban area, left side of Fig. 9(b), most of the roofs are characterized by a

Fig. 9(c) shows the InSAR coherence map of the imaged area obtained using the CSK acquisitions of March 24 and 28, 2011. Light-colored pixels represent values of coherence near 1, while dark pixels represent values near 0. Areas with high coherence indicate the absence of surface perturbation. Places with low coherence, corresponding to the areas disturbed between the two acquisitions dates, are located e.g. in Fig. 9(c)-1, 2 and 3. All roads leading to the holding area of Fig. 9(c)-1 have lost the coherence, due to the movements

The analysis and interpretation of the InSAR coherence map become complicated in the presence of man-made structures. Indeed, for example, the big building in Fig. 9(c)-1 is characterized by a low coherence in Fig. 9(c)-1, which is not truly the change that is of interest. The change classification results (or CCD map) corresponding to the period of 24 and 28 March 2011 are depicted in Fig. 9(d). Only the changes C2, C3 and C4 are of interest, and the other classes help to analyze the scene. Note that the incoherent change detection methods can detect the C2 and C3 changes, but fail to detect the C4 changes (type of change that is revealed by the interferometric phase). Besides the advantages of the CCD technique, the classification contributes to the scene analysis. The large building in Fig. 9(d)-1, and most of the urban area on the left side of Fig.9(d), is now classified as C1 instead of change in the

The analysis of the results of Fig. 9(c) and Fig. 9(d) shows that in a simple environment without obstacles (i.e., open field in the middle of the scene shown in Fig. 9(a)), the classification method identifies the man-made changes as belonging to classes C2, C3 or C4 while the coherence map confuses the changes in a single category. In a complex environment, e. g. of the urban area in the left part of the scene presented in Fig. 9(a) and the airport runway in Fig. 9(c)-4, the InSAR coherence map becomes hard to interpret and the proposed change classification scheme identifies well the man-made changes and contributes (a) (b)

(c) (d)

**Figure 9.** Change classification results. (a) Visible image of the test site. (b) CSK SAR image intensity of March 24, 2011. (c) Coherence map of March 24 and 28, 2011, normal baseline = 307 m. (d) Change classification results (CCD map) between March 24 and 28, 2011. (e) Coherence map of March 28 and April 1, 2011, normal baseline = 65 m. (f) Change classification results (CCD map) between March 28 and April 1, 2011. COSMO-SkyMed TM Product - ASI [2011] processed under license from ASI - Agenzia Spaziale Italiana. All rights reserved.

significantly to the overall scene understanding. The results of Fig. 9(e) and Fig. 9(f), obtained using an other image pair (28 March and 1 April 2011) confirm the validity of the classes proposed in Table 1. All areas of the scene are classified in the same way in Fig. 9(d) and Fig. 9(f), except in the presence of man-made changes, as in Fig. 9(d)-3. In addition, the results show that class C5 is encountered only in the specular surface and shadowed areas; it can be also assimilated to class C1. Despite the complexity of the environment, the invalid classes C6 and C7 are rarely present in the analyzed scene. In general, if classes C6 and C7 are significantly present in the classification result, it is an indication that the value of the intensity threshold should be revised.

### **4. Conclusion**

This work deals with the development and the validation of a new method of coherent change detection and classification. The InSAR coherence map is enhanced and combined with the two corresponding SAR intensity images to build a CCD map using a simple change classification scheme. The proposed method is tested successfully using high resolution COSMO-SkyMed images. The test area of interest concerns the Goma airport, which is a flat and busy test site.

In a complex environment, the obtained results show that the InSAR coherence map reveals changes but remains hard to interpret. The proposed change classification scheme identifies well the man-made changes, and contributes significantly to the overall scene analysis and understanding. The results obtained using other image pairs confirm the validity of the proposed changed-unchanged classes. The proposed method is an improvement for the analysts in charge of the exploitation of information derived from radar imagery.

In future work, we will analyze the influence of the intermediate thresholding of the coherence on the quality of the final CCD map.

### **5. Acknowledgements**

The Authors would like to thank the private companies Sarmap and EXELisvis for their support in providing a full SarscapeTM license (v.4.8, 2011) embedded in the latest version of ENVI software. The COSMO-SkyMed SAR images were obtained through collaboration within the SPARE-SAFE project, launched by the Belgian Ministry of Defense, and processed at the CISS Department of the Royal Military Academy of Belgium.

### **Author details**

Azzedine Bouaraba1, Nada Milisavljevi´c2, Marc Acheroy2, Damien Closson2

1 Radar and Microwave Laboratory, École Militaire Polytechnique, Algiers, Algeria

2 Department of CISS, Royal Military Academy, Brussels, Belgium

### **References**

[1] H. A. Zebker and J. Villasensor. Decorrelation in interferometric radar echoes. *IEEE Trans. and Geosci. Remote Sens.*, Vol. 30(5):950–959, 1992.

[2] E. J. M. Rignot and J. J. V. Zyl. Change detection techniques for ers-1 sar data. *IEEE Trans. and Geosci. Remote Sens.*, Vol. 31(4):896–906, 1993.

14 Land Applications of Radar Remote Sensing

intensity threshold should be revised.

coherence on the quality of the final CCD map.

**4. Conclusion**

and busy test site.

**5. Acknowledgements**

**Author details**

**References**

significantly to the overall scene understanding. The results of Fig. 9(e) and Fig. 9(f), obtained using an other image pair (28 March and 1 April 2011) confirm the validity of the classes proposed in Table 1. All areas of the scene are classified in the same way in Fig. 9(d) and Fig. 9(f), except in the presence of man-made changes, as in Fig. 9(d)-3. In addition, the results show that class C5 is encountered only in the specular surface and shadowed areas; it can be also assimilated to class C1. Despite the complexity of the environment, the invalid classes C6 and C7 are rarely present in the analyzed scene. In general, if classes C6 and C7 are significantly present in the classification result, it is an indication that the value of the

This work deals with the development and the validation of a new method of coherent change detection and classification. The InSAR coherence map is enhanced and combined with the two corresponding SAR intensity images to build a CCD map using a simple change classification scheme. The proposed method is tested successfully using high resolution COSMO-SkyMed images. The test area of interest concerns the Goma airport, which is a flat