Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8897904
Thomas Huang, Maya DeBellis, I. Fenty, P. Heimbach, J. Jacob, O. Wang, Elizabeth Yam
As Alvin Toffler had eloquently put it "You’ve got to think about big things while you’re doing small things, so that all the small things go in the right direction." [6] We have a long history of building many innovative solutions. With a quick search on the web, we can find various tools that offer similar capabilities such as search, visualization, subsetting, analysis, etc. The community is very good with building domain-specific solutions for specific applications. The lack of cohesiveness among these tools introduces technology gaps, which lead to even more stovepipe solutions. An Analytics Center Framework is an architectural concept to establish an extensible, reusable software framework for specific research communities. This paper discusses the application of an open source data analytics framework NASA is developing through its Advanced Information Systems Technology (AIST) program to improve estimating ocean circulation modeling product generation and analysis.
{"title":"Analytics Center Framework for Estimating the Circulation and Climate of the Ocean","authors":"Thomas Huang, Maya DeBellis, I. Fenty, P. Heimbach, J. Jacob, O. Wang, Elizabeth Yam","doi":"10.1109/IGARSS.2019.8897904","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897904","url":null,"abstract":"As Alvin Toffler had eloquently put it \"You’ve got to think about big things while you’re doing small things, so that all the small things go in the right direction.\" [6] We have a long history of building many innovative solutions. With a quick search on the web, we can find various tools that offer similar capabilities such as search, visualization, subsetting, analysis, etc. The community is very good with building domain-specific solutions for specific applications. The lack of cohesiveness among these tools introduces technology gaps, which lead to even more stovepipe solutions. An Analytics Center Framework is an architectural concept to establish an extensible, reusable software framework for specific research communities. This paper discusses the application of an open source data analytics framework NASA is developing through its Advanced Information Systems Technology (AIST) program to improve estimating ocean circulation modeling product generation and analysis.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"69 1","pages":"5355-5358"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88355165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8900610
Chia-Hsiang Wang, Chia-Hsiang Lin, J. Bioucas-Dias, Wei-Cheng Zheng, K. Tseng
In satellite imaging remote sensing, injecting spatial details extracted from a panchromatic image into a multispectral image is referred to as pansharpening, which is ill-posed and requires regularization. Self-similarity, a critical prior knowledge yielding great success in regularizing various imaging inverse problems, has been widely observed in natural images; its formalization is not, however, straightforward. Very recently, we mathematically described the self-similarity pattern as a weighted graph, which can then be transformed into an explicit convex regularizer, that is adopted in our pansharpening criterion design. Most importantly, such convexity allows the adoption of convex optimization theory in solving self-similarity regularized inverse problems with convergence guarantee. One step of our pansharpening algorithm is exactly the proximal operator induced by our new self-similarity regularizer, which is solved by another customized algorithm that is interesting in its own right as could be used as a denoiser. Experiments show promising performance of the proposed method.
{"title":"Panchromatic Sharpening of Multispectral Satellite Imagery Via an Explicitly Defined Convex Self-Similarity Regularization","authors":"Chia-Hsiang Wang, Chia-Hsiang Lin, J. Bioucas-Dias, Wei-Cheng Zheng, K. Tseng","doi":"10.1109/IGARSS.2019.8900610","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900610","url":null,"abstract":"In satellite imaging remote sensing, injecting spatial details extracted from a panchromatic image into a multispectral image is referred to as pansharpening, which is ill-posed and requires regularization. Self-similarity, a critical prior knowledge yielding great success in regularizing various imaging inverse problems, has been widely observed in natural images; its formalization is not, however, straightforward. Very recently, we mathematically described the self-similarity pattern as a weighted graph, which can then be transformed into an explicit convex regularizer, that is adopted in our pansharpening criterion design. Most importantly, such convexity allows the adoption of convex optimization theory in solving self-similarity regularized inverse problems with convergence guarantee. One step of our pansharpening algorithm is exactly the proximal operator induced by our new self-similarity regularizer, which is solved by another customized algorithm that is interesting in its own right as could be used as a denoiser. Experiments show promising performance of the proposed method.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"15 1","pages":"3129-3132"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76918484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8898959
Y. Soldo, D. Vine, E. Dinnat
Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement with in-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30˚. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.
{"title":"Sea Surface Salinity Retrievals from Aquarius Using Neural Networks","authors":"Y. Soldo, D. Vine, E. Dinnat","doi":"10.1109/IGARSS.2019.8898959","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898959","url":null,"abstract":"Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement with in-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30˚. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"8143-8146"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78573261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8900055
B. Hawkins, W. Tung
Synthetic aperture radar (SAR) can provide high-resolution imagery regardless of cloud cover or lighting conditions. These qualities make SAR potentially well-suited for informing response efforts to natural and man-made disasters, but such applications require data products with minimal latency. To meet this challenge, we implemented a real-time SAR processor capable of producing 10 m imagery using an NVIDIA Jetson TX2 embedded GPU module. With its low mass (87 g module) and power consumption under 8 W, the system also holds promise for spaceborne applications.
{"title":"UAVSAR Real-Time Embedded GPU Processor","authors":"B. Hawkins, W. Tung","doi":"10.1109/IGARSS.2019.8900055","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900055","url":null,"abstract":"Synthetic aperture radar (SAR) can provide high-resolution imagery regardless of cloud cover or lighting conditions. These qualities make SAR potentially well-suited for informing response efforts to natural and man-made disasters, but such applications require data products with minimal latency. To meet this challenge, we implemented a real-time SAR processor capable of producing 10 m imagery using an NVIDIA Jetson TX2 embedded GPU module. With its low mass (87 g module) and power consumption under 8 W, the system also holds promise for spaceborne applications.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"42 1","pages":"545-547"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85860653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8899079
M. Zribi, M. Sekhar, S. Bandyopadhyay, S. Bousbih, A. Al Bitar, S. K. Tomer, N. Baghdadi
The abstract should appear at the top of the left-. The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture in agricultural tropical areas. Simultaneously to several radar acquisitions made between June and October 2018, using ALOS2-PALSAR sensor over the Berambadi site (south of India), ground measurements of soil roughness, soil water content, LAI were recorded. The sensitivity of the ALOS-2 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study, even for dense crops. The radar signals are simulated using different types of backscattering models (physical and semi-empirical) over bare soil and vegetation cover for different types of crops (tumeric, etc). WCM model parameterized with LAI for vegetation contribution allows a good estimation of soil moisture for tumeric.
{"title":"Analysis of L Band Radar Data Over Tropical Agricultural Areas","authors":"M. Zribi, M. Sekhar, S. Bandyopadhyay, S. Bousbih, A. Al Bitar, S. K. Tomer, N. Baghdadi","doi":"10.1109/IGARSS.2019.8899079","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899079","url":null,"abstract":"The abstract should appear at the top of the left-. The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture in agricultural tropical areas. Simultaneously to several radar acquisitions made between June and October 2018, using ALOS2-PALSAR sensor over the Berambadi site (south of India), ground measurements of soil roughness, soil water content, LAI were recorded. The sensitivity of the ALOS-2 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study, even for dense crops. The radar signals are simulated using different types of backscattering models (physical and semi-empirical) over bare soil and vegetation cover for different types of crops (tumeric, etc). WCM model parameterized with LAI for vegetation contribution allows a good estimation of soil moisture for tumeric.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"28 1","pages":"6215-6218"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84228859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8899786
Yiwen Wang, Ye Lyu, Yanpeng Cao, M. Yang
As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention. Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application. Previous methods extended single image segmentation approaches to multiple frames. The temporal dependencies are ignored in these methods. This paper proposes a novel segmentation method to solve this problem. Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately. FCN serves as the frame-based segmentation method. Conv-LSTM makes use of the temporal information between consecutive frames. Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV.
{"title":"Deep Learning for Semantic Segmentation of UAV Videos","authors":"Yiwen Wang, Ye Lyu, Yanpeng Cao, M. Yang","doi":"10.1109/IGARSS.2019.8899786","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899786","url":null,"abstract":"As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention. Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application. Previous methods extended single image segmentation approaches to multiple frames. The temporal dependencies are ignored in these methods. This paper proposes a novel segmentation method to solve this problem. Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately. FCN serves as the frame-based segmentation method. Conv-LSTM makes use of the temporal information between consecutive frames. Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"38 1","pages":"2459-2462"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75564388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8898940
D. Raucoules, M. Michele, D. Idier, F. Smaï, M. Foumelis, F. Boulahya, E. Volden, V. Drakopoulou, Przemysław Mujta
This paper presents a method for deriving shallow to intermediate (1m to 50m) coastal bathymetry from space-borne multispectral data taking advantage of the short time-lag between sensors’ bands. The idea is to quantify local waves’ characteristics (wavelengths and celerities) that are related to the water depths using optical data: local spectral analysis can provide the significant wavelengths and inter-band offset-tracking and the corresponding celerities (knowing the inter-band time-lag). Such an approach was firstly described in [1]. However, for an application to extended areas and using large data sets (as possible with the Sentinel-2 archive), a faster technique is required: the ability of processing large areas and data acquired at different dates is required for actual operational uses. The approach we propose here is based on Fast Fourier Transform analysis in order to simultaneously extract the wavelengths and celerities.
{"title":"Bathysent - A Method to Retrieve Coastal Bathymetry from Sentinel-2","authors":"D. Raucoules, M. Michele, D. Idier, F. Smaï, M. Foumelis, F. Boulahya, E. Volden, V. Drakopoulou, Przemysław Mujta","doi":"10.1109/IGARSS.2019.8898940","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898940","url":null,"abstract":"This paper presents a method for deriving shallow to intermediate (1m to 50m) coastal bathymetry from space-borne multispectral data taking advantage of the short time-lag between sensors’ bands. The idea is to quantify local waves’ characteristics (wavelengths and celerities) that are related to the water depths using optical data: local spectral analysis can provide the significant wavelengths and inter-band offset-tracking and the corresponding celerities (knowing the inter-band time-lag). Such an approach was firstly described in [1]. However, for an application to extended areas and using large data sets (as possible with the Sentinel-2 archive), a faster technique is required: the ability of processing large areas and data acquired at different dates is required for actual operational uses. The approach we propose here is based on Fast Fourier Transform analysis in order to simultaneously extract the wavelengths and celerities.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"36 1","pages":"8193-8196"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81076137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8897883
S. Bousbih, M. Zribi, M. El-Hajj, N. Baghdadi, Z. Lili-Chabaane, P. Fanise, G. Boulet
Identifying the irrigated areas is essential for waters managers who are in charge of distributing this resource over a large scale. The monitoring of water soil content and irrigation is a powerful tool for water resource management. The potential of Sentinel-1 (S1) and Sentinel-2 (S2) data for estimating the soil moisture and irrigation is studied over covered surfaces. An inversion algorithm of the Water Cloud Model (WCM) was developed after calibrating and validating the model over the Kairouan plain, a semi-arid region in Tunisia. The aim is to restitute soil moisture over the whole region. The developed algorithm used a synergy between S1, radar data in VV polarization, and NDVI derived from S2 optical data at high spatial resolution. The results showed good accuracy between retrieved and measured soil moisture with a Root Mean Square Error (RMSE) lower than 6 vol.%. Then, the resulting soil moisture maps were used for irrigation mapping. The process used a combination of Support Vector Machine (SVM) and Decision Tree classifications to distinguish between irrigated and non-irrigated agricultural fields. Results from the annual irrigation map show that the overall accuracy on the classification is about 77%.
{"title":"Sentinel-1 and Sentinel-2 Data for Soil Moisture and Irrigation Mapping Over Semi-Arid Region","authors":"S. Bousbih, M. Zribi, M. El-Hajj, N. Baghdadi, Z. Lili-Chabaane, P. Fanise, G. Boulet","doi":"10.1109/IGARSS.2019.8897883","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897883","url":null,"abstract":"Identifying the irrigated areas is essential for waters managers who are in charge of distributing this resource over a large scale. The monitoring of water soil content and irrigation is a powerful tool for water resource management. The potential of Sentinel-1 (S1) and Sentinel-2 (S2) data for estimating the soil moisture and irrigation is studied over covered surfaces. An inversion algorithm of the Water Cloud Model (WCM) was developed after calibrating and validating the model over the Kairouan plain, a semi-arid region in Tunisia. The aim is to restitute soil moisture over the whole region. The developed algorithm used a synergy between S1, radar data in VV polarization, and NDVI derived from S2 optical data at high spatial resolution. The results showed good accuracy between retrieved and measured soil moisture with a Root Mean Square Error (RMSE) lower than 6 vol.%. Then, the resulting soil moisture maps were used for irrigation mapping. The process used a combination of Support Vector Machine (SVM) and Decision Tree classifications to distinguish between irrigated and non-irrigated agricultural fields. Results from the annual irrigation map show that the overall accuracy on the classification is about 77%.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"6 1","pages":"7022-7025"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78239450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8900180
L. Aouf, A. Dalphinet, D. Hauser, L. Delaye, C. Tison, B. Chapron, Laura Hermozo, C. Tourain
The China-France Oceangraphy SATellite (CFOSAT) is an innovative satellite mission with wind and waves measurements on oceans. This paper aims to evaluate the first results of the assimilation of the wave data provided by CFOSAT in the wave model MFWAM. Model runs are implemented during the Calibration/Validation phase of the mission. The results are compared to the wave data from altimeters and buoys. The first results are promising and indicate a significant improvement of wave heights in the different ocean basins (high latitudes, intermediate latitudes and the tropics). Azimuthal cut-off sensitivity tests for the SWIM wave spectra are also examined in this study. We also discussed the impact of combined wave spectra and the ones from the incidence angles of SWIM (6, 8 and 10°). In other respects this study also investigate the complementary use of SWIM and SAR from CFOSAT and SAR from Sentinel-1 for combined assimilation in the wave model MFWAM
{"title":"On the Assimilation of CFOSAT Wave Data in the Wave Model MFWAM : Verification Phase","authors":"L. Aouf, A. Dalphinet, D. Hauser, L. Delaye, C. Tison, B. Chapron, Laura Hermozo, C. Tourain","doi":"10.1109/IGARSS.2019.8900180","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900180","url":null,"abstract":"The China-France Oceangraphy SATellite (CFOSAT) is an innovative satellite mission with wind and waves measurements on oceans. This paper aims to evaluate the first results of the assimilation of the wave data provided by CFOSAT in the wave model MFWAM. Model runs are implemented during the Calibration/Validation phase of the mission. The results are compared to the wave data from altimeters and buoys. The first results are promising and indicate a significant improvement of wave heights in the different ocean basins (high latitudes, intermediate latitudes and the tropics). Azimuthal cut-off sensitivity tests for the SWIM wave spectra are also examined in this study. We also discussed the impact of combined wave spectra and the ones from the incidence angles of SWIM (6, 8 and 10°). In other respects this study also investigate the complementary use of SWIM and SAR from CFOSAT and SAR from Sentinel-1 for combined assimilation in the wave model MFWAM","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"92 1","pages":"7959-7961"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80341215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To date, estimating oil thickness on the sea surface remains a challenge in most cases. When oil thickness estimation using optical data is limited by the absorption properties of the target, a solution consists in combining experimental and airborne hyperspectral data. We developed a method to identify thickness classes from hyperspectral data which, combined with realistic thickness values derived from a pool experiment, allows to estimate slick volume. Hyperspectral images of the same oil emulsion were acquired over a pool and at sea, under real conditions. From the pool data, we derived two classes: the thin and the thick pixels, along with their respective thickness. These classes are then identified on the airborne images acquired during the NOFO campaign by generating a detection mask and using two classification approaches based on spectral indices. The proposed method allows to correctly identify the two thickness classes and, combined with the data from the pool experiment, provides a total slick volume larger than the one derived for the Bonn Agreement Oil Appearance Code.
{"title":"Oil Slick Volume Estimation from Combined Use of Airborne Hyperspectral and Pool Experiment Data","authors":"Roupioz Laure, Viallefont-Robinet Françoise, Miegebielle Véronique","doi":"10.1109/IGARSS.2019.8899057","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899057","url":null,"abstract":"To date, estimating oil thickness on the sea surface remains a challenge in most cases. When oil thickness estimation using optical data is limited by the absorption properties of the target, a solution consists in combining experimental and airborne hyperspectral data. We developed a method to identify thickness classes from hyperspectral data which, combined with realistic thickness values derived from a pool experiment, allows to estimate slick volume. Hyperspectral images of the same oil emulsion were acquired over a pool and at sea, under real conditions. From the pool data, we derived two classes: the thin and the thick pixels, along with their respective thickness. These classes are then identified on the airborne images acquired during the NOFO campaign by generating a detection mask and using two classification approaches based on spectral indices. The proposed method allows to correctly identify the two thickness classes and, combined with the data from the pool experiment, provides a total slick volume larger than the one derived for the Bonn Agreement Oil Appearance Code.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"73 1","pages":"5776-5779"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72647775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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