Pub Date : 2017-07-28DOI: 10.1109/IGARSS.2017.8128303
S. Plank, Martin Jussi, S. Martinis, A. Twele
This article presents a semi-automated methodology for mapping of flooded areas with a special focus on flooded vegetation based on polarimetric Synthetic Aperture Radar (SAR) data. C-band SAR data is well suited for mapping of open water areas, while L-band enables the extraction of detailed information of flooded vegetation. Here, dual-pol C-band data of Sentinel-1 (S-1) is combined with quad-pol L-band ALOS-2/PALSAR-2 data to enable an accurate mapping of the entire flooded area. The developed procedure combines polarimetric decomposition based unsupervised Wishart classification with object-based post-classification refinement as well as the integration of spatial contextual information and global auxiliary data. The methodology was tested at the Evros River (Greek/Turkish border region), where a flooding event occurred in spring 2015.
{"title":"Combining polarimetric sentinel-1 and ALOS-2/PALSAR-2 imagery for mapping of flooded vegetation","authors":"S. Plank, Martin Jussi, S. Martinis, A. Twele","doi":"10.1109/IGARSS.2017.8128303","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8128303","url":null,"abstract":"This article presents a semi-automated methodology for mapping of flooded areas with a special focus on flooded vegetation based on polarimetric Synthetic Aperture Radar (SAR) data. C-band SAR data is well suited for mapping of open water areas, while L-band enables the extraction of detailed information of flooded vegetation. Here, dual-pol C-band data of Sentinel-1 (S-1) is combined with quad-pol L-band ALOS-2/PALSAR-2 data to enable an accurate mapping of the entire flooded area. The developed procedure combines polarimetric decomposition based unsupervised Wishart classification with object-based post-classification refinement as well as the integration of spatial contextual information and global auxiliary data. The methodology was tested at the Evros River (Greek/Turkish border region), where a flooding event occurred in spring 2015.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"19 1","pages":"5705-5708"},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73524668","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 : 2017-07-28DOI: 10.1109/IGARSS.2017.8126911
M. Nannini, M. Martone, P. Rizzoli, P. Prats, M. Rodríguez-Cassola, A. Moreira
This contribution is dedicated to present tomographic investigations on 3D vegetation imaging for future spaceborne SAR missions. The main problem to tackle when performing tomography via repeat-pass spaceborne data is that the temporal decorrelation between acquisitions can be very severe making it difficult to achieve reliable results. In this context, if two or more sensors are available to perform the surveys, a set of quasi-simultaneous data can be achieved for a certain time instant. It is understood that for such data the temporal decorrelation effect as well as the atmospheric artefacts will be strongly mitigated. By varying the acquisition geometry, it is in principle now possible to achieve cross-range resolution and retrieve the vertical profile via SAR tomography. The present paper focuses on a two-satellite scenario like TanDEM-X [1], Tandem-L [2], SAOCOM-CS [3]. In particular, TanDEM-X data, acquired in a pursuit monostatic mode, is employed to perform the demonstration over boreal as well as tropical forest.
{"title":"Spaceborne demonstration of coherent SAR tomography for future companion satellite SAR missions","authors":"M. Nannini, M. Martone, P. Rizzoli, P. Prats, M. Rodríguez-Cassola, A. Moreira","doi":"10.1109/IGARSS.2017.8126911","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8126911","url":null,"abstract":"This contribution is dedicated to present tomographic investigations on 3D vegetation imaging for future spaceborne SAR missions. The main problem to tackle when performing tomography via repeat-pass spaceborne data is that the temporal decorrelation between acquisitions can be very severe making it difficult to achieve reliable results. In this context, if two or more sensors are available to perform the surveys, a set of quasi-simultaneous data can be achieved for a certain time instant. It is understood that for such data the temporal decorrelation effect as well as the atmospheric artefacts will be strongly mitigated. By varying the acquisition geometry, it is in principle now possible to achieve cross-range resolution and retrieve the vertical profile via SAR tomography. The present paper focuses on a two-satellite scenario like TanDEM-X [1], Tandem-L [2], SAOCOM-CS [3]. In particular, TanDEM-X data, acquired in a pursuit monostatic mode, is employed to perform the demonstration over boreal as well as tropical forest.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"7 1","pages":"129-132"},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83342604","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 : 2017-07-28DOI: 10.1109/IGARSS.2017.8127799
John E. Vargas-Muñoz, D. Tuia, J. A. D. Santos, A. Falcão
In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset over Potsdam, Germany. The proposed algorithm outperforms state-of-the-art post classification smoothing algorithms both when the classifier is trained specifically on the image and when it is trained and tested in different set of images.
{"title":"Post classification smoothing in sub-decimeter resolution images with semi-supervised label propagation","authors":"John E. Vargas-Muñoz, D. Tuia, J. A. D. Santos, A. Falcão","doi":"10.1109/IGARSS.2017.8127799","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127799","url":null,"abstract":"In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset over Potsdam, Germany. The proposed algorithm outperforms state-of-the-art post classification smoothing algorithms both when the classifier is trained specifically on the image and when it is trained and tested in different set of images.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"55 1","pages":"3688-3691"},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85228676","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 : 2017-07-28DOI: 10.1109/IGARSS.2017.8128167
Shivangi Srivastava, M. Volpi, D. Tuia
We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and another predicting normalized Digital Surface Model (nDSM) from the pixel values. Since the nDSM/height information is used only in the second loss, there is no need to have a nDSM map at test time, and the model can estimate height automatically on new images. We test our proposed method on a set of sub-decimeter resolution images and show that our model equals the performances of two separate models, but at the cost of a single one.
{"title":"Joint height estimation and semantic labeling of monocular aerial images with CNNS","authors":"Shivangi Srivastava, M. Volpi, D. Tuia","doi":"10.1109/IGARSS.2017.8128167","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8128167","url":null,"abstract":"We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and another predicting normalized Digital Surface Model (nDSM) from the pixel values. Since the nDSM/height information is used only in the second loss, there is no need to have a nDSM map at test time, and the model can estimate height automatically on new images. We test our proposed method on a set of sub-decimeter resolution images and show that our model equals the performances of two separate models, but at the cost of a single one.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"47 1","pages":"5173-5176"},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90489928","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 : 2017-07-28DOI: 10.1109/IGARSS.2017.8127853
A. Marino, Pasquale Iervolino
Extensive work has been carried out on detecting ships using space-borne Synthetic Aperture Radar (SAR) systems. However, the identification of small vessels is still challenging especially when the sea conditions are rough. In this work, a new detector is proposed based on dual-polarized incoherent SAR images. Small ships have a stronger cross polarization accompanied by a higher cross-over co-polarization ratio compared to sea. This is the rational at the base of the detector. The new detector is tested with dual-polarization HH/HV PINGPONG Cosmo-SkyMed images acquired over the North Sea. The test area is near Rotterdam where a large number of ships are expected.
{"title":"Ship detection with Cosmo-SkyMed PINGPONG data using the dual-pol ratio anomaly detector","authors":"A. Marino, Pasquale Iervolino","doi":"10.1109/IGARSS.2017.8127853","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127853","url":null,"abstract":"Extensive work has been carried out on detecting ships using space-borne Synthetic Aperture Radar (SAR) systems. However, the identification of small vessels is still challenging especially when the sea conditions are rough. In this work, a new detector is proposed based on dual-polarized incoherent SAR images. Small ships have a stronger cross polarization accompanied by a higher cross-over co-polarization ratio compared to sea. This is the rational at the base of the detector. The new detector is tested with dual-polarization HH/HV PINGPONG Cosmo-SkyMed images acquired over the North Sea. The test area is near Rotterdam where a large number of ships are expected.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1982 1","pages":"3897-3900"},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90288829","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 : 2017-07-28DOI: 10.1109/IGARSS.2017.8128301
W. Cao, S. Martinis, S. Plank
Given the proven effectiveness of the split-based approach (SBA) for SAR image analysis in literature, the objective of this article focuses on designing a more efficient and robust version of the SBA for applications in the context of rapid flood mapping. A hierarchical tile-ranking SBA is proposed in this paper which is combined with a previous multilevel tile contrast analysis to significantly reduce the amount of data for the estimation of global threshold. A separability test is further applied to reject badly located tiles. The classification is optimized by merging pixel backscatter values, cluster size and local slope into a fuzzy-logic based post-classification framework. The proposed method was tested on Sentinel-1 SAR data acquired over Lake Liambezi in the Caprivi strip of Namibia and validated with respect to a Landsat-8 scene. Compared to tiles selected by the conventional SBA the proposed method automatically select better relevant ones and the classification is more robust with less misclassification of water-lookalikes.
{"title":"Automatic SAR-based flood detection using hierarchical tile-ranking thresholding and fuzzy logic","authors":"W. Cao, S. Martinis, S. Plank","doi":"10.1109/IGARSS.2017.8128301","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8128301","url":null,"abstract":"Given the proven effectiveness of the split-based approach (SBA) for SAR image analysis in literature, the objective of this article focuses on designing a more efficient and robust version of the SBA for applications in the context of rapid flood mapping. A hierarchical tile-ranking SBA is proposed in this paper which is combined with a previous multilevel tile contrast analysis to significantly reduce the amount of data for the estimation of global threshold. A separability test is further applied to reject badly located tiles. The classification is optimized by merging pixel backscatter values, cluster size and local slope into a fuzzy-logic based post-classification framework. The proposed method was tested on Sentinel-1 SAR data acquired over Lake Liambezi in the Caprivi strip of Namibia and validated with respect to a Landsat-8 scene. Compared to tiles selected by the conventional SBA the proposed method automatically select better relevant ones and the classification is more robust with less misclassification of water-lookalikes.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"25 1","pages":"5697-5700"},"PeriodicalIF":0.0,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78230411","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 : 2017-07-27DOI: 10.1109/IGARSS.2017.8127851
D. Velotto, A. Marino, F. Nunziata
Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for maritime safety and security. In this framework, monitoring oil and gas offshore platforms is a key topic taken into account the high risk of accident, e.g. exposed to extreme weather conditions, and the potential threats to the environment, e.g. release of polluting material into the ocean. In this study, offshore platform monitoring is discussed using multi-polarization X-band SAR imagery. For operational purposes, the analysis is undertaken using a data set of dual-polarization TerraSAR-X/TanDEM-X (TS-X/TD-X) imagery collected over a test site in Gulf of Mexico at low and high incidence angles. Additionally, experimental Dual Receive Antenna (DRA) bistatic quad-polarization TD-X data will be used for a more in depth analysis of the backscattering properties and detection performance of different target polarimetric detectors. The motivation behind this work is the observation that, under low incidence angle and moderate wind conditions, co-polarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. The multi-temporal dataset allows investigating the possible causes of this unexpected behavior and to draw some conclusions on the target's backscattering depending on polarization, resolution and incidence angle.
{"title":"Backscattering analysis of offshore platforms in gulf of Mexico via multi-polarization TerraSAR-X/TanDEM-X data","authors":"D. Velotto, A. Marino, F. Nunziata","doi":"10.1109/IGARSS.2017.8127851","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127851","url":null,"abstract":"Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for maritime safety and security. In this framework, monitoring oil and gas offshore platforms is a key topic taken into account the high risk of accident, e.g. exposed to extreme weather conditions, and the potential threats to the environment, e.g. release of polluting material into the ocean. In this study, offshore platform monitoring is discussed using multi-polarization X-band SAR imagery. For operational purposes, the analysis is undertaken using a data set of dual-polarization TerraSAR-X/TanDEM-X (TS-X/TD-X) imagery collected over a test site in Gulf of Mexico at low and high incidence angles. Additionally, experimental Dual Receive Antenna (DRA) bistatic quad-polarization TD-X data will be used for a more in depth analysis of the backscattering properties and detection performance of different target polarimetric detectors. The motivation behind this work is the observation that, under low incidence angle and moderate wind conditions, co-polarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. The multi-temporal dataset allows investigating the possible causes of this unexpected behavior and to draw some conclusions on the target's backscattering depending on polarization, resolution and incidence angle.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"27 1","pages":"3890-3893"},"PeriodicalIF":0.0,"publicationDate":"2017-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82860189","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 : 2017-07-27DOI: 10.1109/IGARSS.2017.8128021
M. Dabboor, S. Singha, K. Topouzelis, D. Flett
Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for maritime pollution monitoring with three major requirements: 1) low noise floor, 2) large area coverage, and 3) polarization diversity to maximize detection and discrimination of pollution features. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP for four RCM SAR modes for oil spill detection. These modes have different spatial resolutions and noise floors. An initial visual interpretation of the results indicates potential of some CP features for the discrimination between oil spills and lookalike.
{"title":"Oil spill detection using simulated radarsat constellation mission compact polarimetric SAR data","authors":"M. Dabboor, S. Singha, K. Topouzelis, D. Flett","doi":"10.1109/IGARSS.2017.8128021","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8128021","url":null,"abstract":"Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for maritime pollution monitoring with three major requirements: 1) low noise floor, 2) large area coverage, and 3) polarization diversity to maximize detection and discrimination of pollution features. In order to reconcile the advantages of fully polarimetric SAR with larger area coverage, compact polarimetry (CP) acquisitions offer a trade-off between the above mentioned requirements. The future Canadian RADARSAT Constellation Mission (RCM) will enable the acquisition of CP SAR data in wide swath imagery, including ScanSAR modes. In this study, we investigate the potential of CP for four RCM SAR modes for oil spill detection. These modes have different spatial resolutions and noise floors. An initial visual interpretation of the results indicates potential of some CP features for the discrimination between oil spills and lookalike.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1 1","pages":"4582-4585"},"PeriodicalIF":0.0,"publicationDate":"2017-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88813647","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 : 2017-07-26DOI: 10.1109/IGARSS.2017.8127964
M. Piles, Gustau Camps-Valls, D. Chaparro, D. Entekhabi, A. Konings, T. Jagdhuber
The ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and prediction capabilities. Core agricultural regions have been selected worldwide covering major crops (corn, soybean, wheat, rice). The complementarity and synergies between the microwave vegetation signal, sensitive to biomass water-uptake dynamics, and optical indices, sensitive to canopy greenness, are explored. Results reveal the value of L-band VOD as an independent ecological indicator for global terrestrial biosphere studies. 1
{"title":"Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices","authors":"M. Piles, Gustau Camps-Valls, D. Chaparro, D. Entekhabi, A. Konings, T. Jagdhuber","doi":"10.1109/IGARSS.2017.8127964","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127964","url":null,"abstract":"The ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and prediction capabilities. Core agricultural regions have been selected worldwide covering major crops (corn, soybean, wheat, rice). The complementarity and synergies between the microwave vegetation signal, sensitive to biomass water-uptake dynamics, and optical indices, sensitive to canopy greenness, are explored. Results reveal the value of L-band VOD as an independent ecological indicator for global terrestrial biosphere studies. 1","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"342 1","pages":"4346-4349"},"PeriodicalIF":0.0,"publicationDate":"2017-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76913413","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 : 2017-07-26DOI: 10.1109/IGARSS.2017.8127621
S. Rikka, A. Pleskachevsky, R. Uiboupin, S. Jacobsen
In this work, remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X and Tandem-X (TS-X and TD-X) satellites have been used to estimate total significant wave height and surface wind speed in various areas in the Eastern Baltic Sea to further improve empirical XWAVE_C algorithm and to investigate the wave behaviour and local variability. In total, 91 TS-X StripMap scenes between 2012 and 2016 were processed and analysed. The wave height results from SAR images were compared with collocated in situ buoy measurements from different timeframe and locations. The analysed data include both high and low wind sea. New corrections using local wind speed, simultaneously estimated from the same subscene, were introduced to further improve XWAVE_C wave height estimation for short wave systems dominate in the Baltic Sea. The comparison of SAR-based wave height with measured wave height showed high agreement with correlation r of 0.89.
{"title":"Sea state parameters in highly variable environment of baltic sea from satellite radar images","authors":"S. Rikka, A. Pleskachevsky, R. Uiboupin, S. Jacobsen","doi":"10.1109/IGARSS.2017.8127621","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127621","url":null,"abstract":"In this work, remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X and Tandem-X (TS-X and TD-X) satellites have been used to estimate total significant wave height and surface wind speed in various areas in the Eastern Baltic Sea to further improve empirical XWAVE_C algorithm and to investigate the wave behaviour and local variability. In total, 91 TS-X StripMap scenes between 2012 and 2016 were processed and analysed. The wave height results from SAR images were compared with collocated in situ buoy measurements from different timeframe and locations. The analysed data include both high and low wind sea. New corrections using local wind speed, simultaneously estimated from the same subscene, were introduced to further improve XWAVE_C wave height estimation for short wave systems dominate in the Baltic Sea. The comparison of SAR-based wave height with measured wave height showed high agreement with correlation r of 0.89.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"1 1","pages":"2965-2968"},"PeriodicalIF":0.0,"publicationDate":"2017-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80685663","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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