Roughness of soil surface not only performs a remarkable influence to the BRDF of ground , and it is variable as well . So it ’s one of important factors that affect the precision of remote sensing for soil water content . In this paper , it is demonstrated that the relationship between soil reflected spectrum and the soil water content is about obey the Beer Law , and then the absorption coefficient curve of moisture in soil was measured . It indicates that the proposed classification method is not subject to the dimensionality and form of data sources , and it can integrate the data source information to improve the classification accuracy , so that it is very useful to apply multi-source data and prior knowledge to land cover classification in mountainous regions . ( C ) 2011 International Society for Photogrammetry and Remote Sensing , Inc. ( ISPRS ) Published by Elsevier B.V. All rights reserved . However , all semianalytical models using the red to green spectral ratio have demonstrated approximately the same accuracy and correlation as empirical models , what provides an additional support for using more simple easily calculated empirical models . Additionally , relationships between SSC and inherent optical properties ( IOPs ) ( absorption and backscattering coefficients ) and between IOPs and Rrs(λ ) provided by the semianalytical models have their own benefits for aquatic optics and remote sensing purposes . The algorithm preserves photometry , can weight input images according to the statistical significance of each pixel , and removes the effect of geometric distortion on both image shape and photometry . In this paper , we describe its development for remote sensing purposes , show the usefulness of the algorithm working with images as different to the astronomical images as the remote sensing ones , and show applications to : 1 ) a set of simulated multispectral images obtained from a real Quickbird image ; and 2 ) a set of multispectral real Landsat Enhanced Thematic Mapper Plus ( ETM+ ) images . These examples show that the algorithm provides a substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images , without amplifying the noise , and with very few artifacts In many cases , not only are many observations missing , but the available also quantized to a predefined number of bits . Quantization is an integral part of data acquisition , especially for remote sensing scenario like communiting measurements from airborne and space-borne platform where channel bandwidth is very limited , as well as application involving energy-limited systems such as Wireless Sensor Networks and Internet of Things platforms , where full data transmission is directly related to an increase in power consumption and a subsequent reduction in the network lifetime . As a result , quantization of a signal is a critical step for high dimensional data transmission but also for data storage . GeoSTAR will satisfy a number of important measurement objectives , many of them identified by NOAA as unmet needs in their GOES-R pre-planned product improvements ( P3I ) lists and others by NASA in their research roadmaps and as discussed in a white paper submitted to the NRC Decadal Survey . The performance of the prototype has been outstanding , and this proof of concept represents a major breakthrough in remote sensing capabilities . The GeoSTAR concept is now at a stage of development where an infusion into space systems can be initiated , either on a NASA sponsored research mission or on a NOAA sponsored operational mission . This project aims to advance the application of Remote Sensing in the context of managing water resources by integrating high resolution remote sensing technology into a Knowledge Management System for the management of water resources and the infrastructure to control them . Remote sensing technology is used to complement the development of a knowledge management system by advancing the remote sensing capability and image processing to near-real time . This capability can be used to monitor and assess a river basin to develop flood extent and predictions , but also to assess hydrological capacities for hydro-power generation . This project aims to advance the application of Remote Sensing in the context of managing water resource by integrating high resolution remote sensing technology into a Knowledge Management System for the management of water resources and the infrastructure to control them . Remote Sensing Technology is used to complement the development of a Knowledge Management System by advancing the remote sensing capability and image processing to near-real time . This capability can be used to monitor and assess a river basin to develop flood extent and predictions . The environments such as inaccessible places in forests and deserts obstruct free access to human beings and call for remote access by electronic means to get the geographical nature . This research project deals with the establishment of a system for remotely sensing and gathering the information . Three locations , which reveal the geography in a better manner is decided first and three portable radars are launched into the spots from air As soon as the radars stabilize their positions they start functioning to scan electronically the environment . This paper presents the forest ecological and modelling aspects of the research . A related paper presents the remote sensing aspects , including application of the model to map predicted structural complexity , map validation , and testing of the method at multiple scales . Super resolution ( SR ) image reconstruction is a technique to recover a high resolution image from several low resolution images using the additional information among them . In this paper , we introduce the applic-ations of SR techniques to remote sensing observing systems . We also give a SR method to multi-temporal remote sensing images . The advent of routine collection of high-quality digital photography provides for traditional uses , as well as “ remote sensing ” uses such as the monitoring of environmental indicators . A well-devised monitoring system , based on consistent data and methods , provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible . It is pointed out that full-color three-dimensional imaging for remote visual sensing can be achieved through recent advances in alternating-frame technology that are applicable to video , computer graphics , infrared video , and film . Advantages are real-time display with a two-source system and near-real-time display with a single-camera system . The problem of low - rank recovery from sparse observations is referred as the matrix completion problem . Several important real - world problems can be cast as a matrix completion problem , including remote sensing , system identification and recommendation systems . Throughout the paper , we will consider the recommendation system problem as an illustration of the matrix completion problem . section : Introduction Hyperspectral image ( HSI ) has attracted considerable attention in the remote sensing community and been widely used in various areas . With the rich spectral information in HSI , different land cover categories can potentially be differentiated precisely . Textures that are transformed can also be classified , an example is presented using a wavelet algorithm . Segmentation of remote sensing images is discussed using the co-occurrence matrix method . Classification with extrema features is demonstrated for different sets of images . An improved edge linking method for segmentation of remotely sensed imagery is proposed in this paper . Firstly , the gradient strength and the gradient direction are calculated . The TV model is an effective tool for image processing operations such as restoration , enhancement , reconstruction , and diffusion . Due to the relationship between the TV model and the segmentation problem , in this letter , a TV-based approach is investigated for segmentation of high-spatial-resolution remote-sensing imagery . Subsequently , an object-based classification method , i.e. , majority voting , is used to classify the segmented results . Remote sensing imagery segmentation is a process of dividing an image into different regions such that each region is , but the union of any two adjacent regions is not , homogeneous . It is one of the key techniques in the object-oriented remote sensing imagery data mining and its application , also quite essential in remote sensing image processing engineering . This mixing brings up untrustworthy shoreline results . Furthermore , problems due to segmentation of high-resolution remote sensing imagery tend to make shoreline detection results erroneous . In this study , a robust two-staged shoreline extraction algorithm is proposed . This chapter focuses on segmentation of remotely sensed image data and object-based image analysis . It discusses the differences between pixel-based and object-based image analysis ; the potential of the object-based approach ; and , the application of eCognition software for performing image segmentation and classification at different levels of detail . This paper compares the discrimination ability of two texture analysis methods : Markov random fields ( MRFs ) and gray-level cooccurrence probabilities ( GLCPs ) . There exists limited published research comparing different texture methods , especially with regard to segmenting remotely sensed imagery . The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated . As an active microwave imaging sensor for the high-resolution earth observation , synthetic aperture radar ( SAR ) has been extensively applied in military , agriculture , geology , ecology , oceanography , etc . , due to its prominent advantages of all-weather and all-time working capacity . Especially , in the marine field , SAR can provide numerous high-quality services for fishery management , traffic control , sea-ice monitoring , marine environmental protection , etc . Among them , ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation . Multi-temporal satellite images are a natural data source to survey ice on lakes . In this paper , we describe a method for lake ice monitoring , which uses low spatial resolution ( 250 m–1000 m ) satellite images to determine whether a lake is frozen or not . We report results on four selected lakes in Switzerland : Sihl , Sils , Silvaplana and St. Moritz . As a pre-processing step , the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images . We define the lake ice detection as a two-class ( frozen , non-frozen ) semantic segmentation problem . Several spectral channels of the multi-spectral satellite data are used , both reflective and emissive ( thermal ) . We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar ( SAR ) data with a deep neural network . In previous studies that used optical satellite imagery for lake ice monitoring , frequent cloud cover was a main limiting factor , which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions . We cast ice detection as a two class ( frozen , non-frozen ) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network ( CNN ) . TanDEM-X operated in its pursuit monostatic mode for a limited time in support of scientific studies . The introduction of a system with a fully operational capacity to acquire near-simultaneous InSAR images would benefit operational users concerned with lake ice extent mapping and — we expect — many others that deal with water or other rapidly changing features . During our 2002 winter experiment ( GLAWEX 2002 ) NASA AIRSAR C-band and L-band polarimetric and interferometric measurements are collected over Lake Superior , Green Bay and northern Lake Michigan , and the North Channel together with in-situ field observations . Polarimetric C-band SAR data obtained form GLAWEX 1997 ( shipborne radar ) and GLAWEX 2002 ( airborne SAR ) are used to develop multi-polarization algorithms in view of ENVISAT and RADARSAT-2 multi-polarization SAR applications to lake ice mapping . Furthermore , interferometric SAR data acquired during GLAWEX 2002 are used to evaluate the utility of interferometric SAR data for three-dimensional mapping for lake ice including aerial coverage and thickness . Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins , approximately 0.26 m in ice . Although not designed for lake ice studies , CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes . The microwave propagation time $ \tau_{delay}$ through the pack yields a measure of its vertical extent ; thus , this technique is a direct measurement of depth . However , the presence of a different low loss layer on the lake icepack such as dry snowpack introduces another multi-path interference , which can effect the lake icepack thickness measurement . We present a simple geophysical forward model for the multipath interference phenomenon and derive the WiBAR system requirements needed to correctly measure the icepack thickness . The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset . Overall , the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications : for instance , derivation of shallow lakes bathymetry , evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost . ASIRAS is an airborne system designed for sea-ice and ice sheet monitoring . It has been developed by ESA under a Technology Research Program contract . In particular , owning to its 6-day revisit time and its innovative acquisition mode , which is referred to as Terrain Observation by Progressive Scans ( TOPS ) and is fundamental for guaranteeing a global spatial coverage , Sentinel-1 constellation is contributing to the creation of a framework for the exploitation of “ Big Data ” for Earth Observation ( EO ) applications . In this paper , we present an efficient and automatic implementation of the Parallel Small BAseline Subset ( P-SBAS ) DInSAR algorithm , specifically intended for the processing of Sentinel-1 SAR data . The algorithm is able to run on distributed computing infrastructures by effectively exploiting a large number of resources , and allows the generation of ground displacement time-series . The methodology , allows the full exploitation of Sentinel-1 constellation data ( wide area coverage , high temporal repeatability , freely available data ) making feasible long term monitoring plannings . Moreover a free software tool for the Sentinel-1 SAR data processing has been developed . This work presents the developed methodology , through some images of the most significant results obtained over the two test sites of the project ( the Canary Island , in Spain , and the Volterra municipality , in Italy ) , and explains both the strengths and the main lessons learnt for the future steps .
Roughness of soil surface not only performs a remarkable influence to the BRDF of ground , and it is variable as well . So it ’s one of important factors that affect the precision of remote sensing for soil water content . In this paper , it is demonstrated that the relationship between soil reflected spectrum and the soil water content is about obey the Beer Law , and then the absorption coefficient curve of moisture in soil was measured .
It indicates that the proposed classification method is not subject to the dimensionality and form of data sources , and it can integrate the data source information to improve the classification accuracy , so that it is very useful to apply multi-source data and prior knowledge to land cover classification in mountainous regions . ( C ) 2011 International Society for Photogrammetry and Remote Sensing , Inc. ( ISPRS ) Published by Elsevier B.V. All rights reserved .
However , all semianalytical models using the red to green spectral ratio have demonstrated approximately the same accuracy and correlation as empirical models , what provides an additional support for using more simple easily calculated empirical models . Additionally , relationships between SSC and inherent optical properties ( IOPs ) ( absorption and backscattering coefficients ) and between IOPs and Rrs(λ ) provided by the semianalytical models have their own benefits for aquatic optics and remote sensing purposes .
The algorithm preserves photometry , can weight input images according to the statistical significance of each pixel , and removes the effect of geometric distortion on both image shape and photometry . In this paper , we describe its development for remote sensing purposes , show the usefulness of the algorithm working with images as different to the astronomical images as the remote sensing ones , and show applications to : 1 ) a set of simulated multispectral images obtained from a real Quickbird image ; and 2 ) a set of multispectral real Landsat Enhanced Thematic Mapper Plus ( ETM+ ) images . These examples show that the algorithm provides a substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images , without amplifying the noise , and with very few artifacts
In many cases , not only are many observations missing , but the available also quantized to a predefined number of bits . Quantization is an integral part of data acquisition , especially for remote sensing scenario like communiting measurements from airborne and space-borne platform where channel bandwidth is very limited , as well as application involving energy-limited systems such as Wireless Sensor Networks and Internet of Things platforms , where full data transmission is directly related to an increase in power consumption and a subsequent reduction in the network lifetime . As a result , quantization of a signal is a critical step for high dimensional data transmission but also for data storage .
GeoSTAR will satisfy a number of important measurement objectives , many of them identified by NOAA as unmet needs in their GOES-R pre-planned product improvements ( P3I ) lists and others by NASA in their research roadmaps and as discussed in a white paper submitted to the NRC Decadal Survey . The performance of the prototype has been outstanding , and this proof of concept represents a major breakthrough in remote sensing capabilities . The GeoSTAR concept is now at a stage of development where an infusion into space systems can be initiated , either on a NASA sponsored research mission or on a NOAA sponsored operational mission .
This project aims to advance the application of Remote Sensing in the context of managing water resources by integrating high resolution remote sensing technology into a Knowledge Management System for the management of water resources and the infrastructure to control them . Remote sensing technology is used to complement the development of a knowledge management system by advancing the remote sensing capability and image processing to near-real time . This capability can be used to monitor and assess a river basin to develop flood extent and predictions , but also to assess hydrological capacities for hydro-power generation .
This project aims to advance the application of Remote Sensing in the context of managing water resource by integrating high resolution remote sensing technology into a Knowledge Management System for the management of water resources and the infrastructure to control them . Remote Sensing Technology is used to complement the development of a Knowledge Management System by advancing the remote sensing capability and image processing to near-real time . This capability can be used to monitor and assess a river basin to develop flood extent and predictions .
The environments such as inaccessible places in forests and deserts obstruct free access to human beings and call for remote access by electronic means to get the geographical nature . This research project deals with the establishment of a system for remotely sensing and gathering the information . Three locations , which reveal the geography in a better manner is decided first and three portable radars are launched into the spots from air As soon as the radars stabilize their positions they start functioning to scan electronically the environment .
This paper presents the forest ecological and modelling aspects of the research . A related paper presents the remote sensing aspects , including application of the model to map predicted structural complexity , map validation , and testing of the method at multiple scales .
Super resolution ( SR ) image reconstruction is a technique to recover a high resolution image from several low resolution images using the additional information among them . In this paper , we introduce the applic-ations of SR techniques to remote sensing observing systems . We also give a SR method to multi-temporal remote sensing images .
The advent of routine collection of high-quality digital photography provides for traditional uses , as well as “ remote sensing ” uses such as the monitoring of environmental indicators . A well-devised monitoring system , based on consistent data and methods , provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible .
It is pointed out that full-color three-dimensional imaging for remote visual sensing can be achieved through recent advances in alternating-frame technology that are applicable to video , computer graphics , infrared video , and film . Advantages are real-time display with a two-source system and near-real-time display with a single-camera system .
The problem of low - rank recovery from sparse observations is referred as the matrix completion problem . Several important real - world problems can be cast as a matrix completion problem , including remote sensing , system identification and recommendation systems . Throughout the paper , we will consider the recommendation system problem as an illustration of the matrix completion problem .
section : Introduction Hyperspectral image ( HSI ) has attracted considerable attention in the remote sensing community and been widely used in various areas . With the rich spectral information in HSI , different land cover categories can potentially be differentiated precisely .
Textures that are transformed can also be classified , an example is presented using a wavelet algorithm . Segmentation of remote sensing images is discussed using the co-occurrence matrix method . Classification with extrema features is demonstrated for different sets of images .
An improved edge linking method for segmentation of remotely sensed imagery is proposed in this paper . Firstly , the gradient strength and the gradient direction are calculated .
The TV model is an effective tool for image processing operations such as restoration , enhancement , reconstruction , and diffusion . Due to the relationship between the TV model and the segmentation problem , in this letter , a TV-based approach is investigated for segmentation of high-spatial-resolution remote-sensing imagery . Subsequently , an object-based classification method , i.e. , majority voting , is used to classify the segmented results .
Remote sensing imagery segmentation is a process of dividing an image into different regions such that each region is , but the union of any two adjacent regions is not , homogeneous . It is one of the key techniques in the object-oriented remote sensing imagery data mining and its application , also quite essential in remote sensing image processing engineering .
This mixing brings up untrustworthy shoreline results . Furthermore , problems due to segmentation of high-resolution remote sensing imagery tend to make shoreline detection results erroneous . In this study , a robust two-staged shoreline extraction algorithm is proposed .
This chapter focuses on segmentation of remotely sensed image data and object-based image analysis . It discusses the differences between pixel-based and object-based image analysis ; the potential of the object-based approach ; and , the application of eCognition software for performing image segmentation and classification at different levels of detail .
This paper compares the discrimination ability of two texture analysis methods : Markov random fields ( MRFs ) and gray-level cooccurrence probabilities ( GLCPs ) . There exists limited published research comparing different texture methods , especially with regard to segmenting remotely sensed imagery . The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated .
As an active microwave imaging sensor for the high-resolution earth observation , synthetic aperture radar ( SAR ) has been extensively applied in military , agriculture , geology , ecology , oceanography , etc . , due to its prominent advantages of all-weather and all-time working capacity . Especially , in the marine field , SAR can provide numerous high-quality services for fishery management , traffic control , sea-ice monitoring , marine environmental protection , etc . Among them , ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation .
Multi-temporal satellite images are a natural data source to survey ice on lakes . In this paper , we describe a method for lake ice monitoring , which uses low spatial resolution ( 250 m–1000 m ) satellite images to determine whether a lake is frozen or not . We report results on four selected lakes in Switzerland : Sihl , Sils , Silvaplana and St. Moritz .
As a pre-processing step , the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images . We define the lake ice detection as a two-class ( frozen , non-frozen ) semantic segmentation problem . Several spectral channels of the multi-spectral satellite data are used , both reflective and emissive ( thermal ) .
We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar ( SAR ) data with a deep neural network . In previous studies that used optical satellite imagery for lake ice monitoring , frequent cloud cover was a main limiting factor , which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions . We cast ice detection as a two class ( frozen , non-frozen ) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network ( CNN ) .
TanDEM-X operated in its pursuit monostatic mode for a limited time in support of scientific studies . The introduction of a system with a fully operational capacity to acquire near-simultaneous InSAR images would benefit operational users concerned with lake ice extent mapping and — we expect — many others that deal with water or other rapidly changing features .
During our 2002 winter experiment ( GLAWEX 2002 ) NASA AIRSAR C-band and L-band polarimetric and interferometric measurements are collected over Lake Superior , Green Bay and northern Lake Michigan , and the North Channel together with in-situ field observations . Polarimetric C-band SAR data obtained form GLAWEX 1997 ( shipborne radar ) and GLAWEX 2002 ( airborne SAR ) are used to develop multi-polarization algorithms in view of ENVISAT and RADARSAT-2 multi-polarization SAR applications to lake ice mapping . Furthermore , interferometric SAR data acquired during GLAWEX 2002 are used to evaluate the utility of interferometric SAR data for three-dimensional mapping for lake ice including aerial coverage and thickness .
Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins , approximately 0.26 m in ice . Although not designed for lake ice studies , CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes .
The microwave propagation time $ \tau_{delay}$ through the pack yields a measure of its vertical extent ; thus , this technique is a direct measurement of depth . However , the presence of a different low loss layer on the lake icepack such as dry snowpack introduces another multi-path interference , which can effect the lake icepack thickness measurement . We present a simple geophysical forward model for the multipath interference phenomenon and derive the WiBAR system requirements needed to correctly measure the icepack thickness .
The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset . Overall , the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications : for instance , derivation of shallow lakes bathymetry , evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost .
ASIRAS is an airborne system designed for sea-ice and ice sheet monitoring . It has been developed by ESA under a Technology Research Program contract .
In particular , owning to its 6-day revisit time and its innovative acquisition mode , which is referred to as Terrain Observation by Progressive Scans ( TOPS ) and is fundamental for guaranteeing a global spatial coverage , Sentinel-1 constellation is contributing to the creation of a framework for the exploitation of “ Big Data ” for Earth Observation ( EO ) applications . In this paper , we present an efficient and automatic implementation of the Parallel Small BAseline Subset ( P-SBAS ) DInSAR algorithm , specifically intended for the processing of Sentinel-1 SAR data . The algorithm is able to run on distributed computing infrastructures by effectively exploiting a large number of resources , and allows the generation of ground displacement time-series .
The methodology , allows the full exploitation of Sentinel-1 constellation data ( wide area coverage , high temporal repeatability , freely available data ) making feasible long term monitoring plannings . Moreover a free software tool for the Sentinel-1 SAR data processing has been developed . This work presents the developed methodology , through some images of the most significant results obtained over the two test sites of the project ( the Canary Island , in Spain , and the Volterra municipality , in Italy ) , and explains both the strengths and the main lessons learnt for the future steps .
Research on remote sensing model for soil water content on rough surface Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions Evaluation of Empirical and Semianalytical Spectral Reflectance Models for Surface Suspended Sediment Concentration in the Highly Variable Estuarine and Coastal Waters of East China Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear Reconstruction 1-Bit Tensor Completion GeoSTAR: a microwave sounder for geostationary applications Integrating high resolution remote sensing technology into an advanced water resources knowledge management system Developing high resolution remote sensing technology into an advanced knowledge management system to monitor and assess water resources Remote sensing of a geographical environment with portable radars and satellite link Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic data 1 Super Resolution Image Reconstruction Applied in Remote Sensing Empirical Models for Radiometric Calibration of Digital Aerial Frame Mosaics Advanced alternating-frame technology (VISIDEP) and three-dimensional remote sensing Matrix Completion on Graphs Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification The use of a robust toolbox for texture classification and segmentation Sequential edge linking method for segmentation of remotely sensed imagery based on heuristic search An Energy-Driven Total Variation Model for Segmentation and Classification of High Spatial Resolution Remote-Sensing Imagery A Survey on Segmentation Techniques and Application Strategy of High Spatial Resolution Remote Sensing Imagery A robust approach for shoreline detection in satellite imagery Segmentation and Object-Based Image Analysis Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection LAKE ICE DETECTION IN LOW-RESOLUTION OPTICAL SATELLITE IMAGES Lake Ice Detection from Sentinel-1 SAR with Deep Learning InSAR coherence for automated lake ice extent mapping: TanDEM-X bistatic and pursuit monostatic results Radar remote sensing of Great Lakes ice cover Retrievals of Lake Ice Thickness From Great Slave Lake and Great Bear Lake Using CryoSat-2 Effect of a Thin DRY Snow Layer on the Lake ICE Thickness Measurement using Wideband Autocorrelation Radiometry Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series Development of ASIRAS (Airborne SAR/Interferometric Altimeter System) Automatic and Systematic Sentinel-1 SBAS-DInSAR Processing Chain for Deformation Time-series Generation SAFETY PROJECT: SENTINEL-1 BASED TOOLS FOR GEOHAZARDS MONITORING AND MANAGEMENT
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IGARSS Image Processing: Algorithms and Systems NIPS 2014 ArXiv Geoinformatics SIU IGARSS IGARSS IGARSS
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