Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR
In recent years SAR Polarimetry has become a valuable tool in space-borne SAR based sea ice analysis. This work compares the polarimetric backscatter behavior of sea ice in space-borne X-band C-band and L-band Synthetic Aperture Radar (SAR) imagery. Two sets of spatially and temporally near coincide...
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ftdlr:oai:elib.dlr.de:110693 2024-05-19T07:48:14+00:00 Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR Singha, Suman 2017-07-24 application/pdf https://elib.dlr.de/110693/ https://elib.dlr.de/110693/2/SINGHA_IGARSS_2017.pdf https://doi.org/10.1109/IGARSS.2017.8126965 en eng IEEE Xplore https://elib.dlr.de/110693/2/SINGHA_IGARSS_2017.pdf Singha, Suman (2017) Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 338-341. IEEE Xplore. IGARSS 2017, 2017-07-23 - 2017-07-28, Fort Worth, Texas, USA. doi:10.1109/IGARSS.2017.8126965 <https://doi.org/10.1109/IGARSS.2017.8126965>. ISSN 2153-7003. SAR-Signalverarbeitung Konferenzbeitrag PeerReviewed 2017 ftdlr https://doi.org/10.1109/IGARSS.2017.8126965 2024-04-25T00:40:10Z In recent years SAR Polarimetry has become a valuable tool in space-borne SAR based sea ice analysis. This work compares the polarimetric backscatter behavior of sea ice in space-borne X-band C-band and L-band Synthetic Aperture Radar (SAR) imagery. Two sets of spatially and temporally near coincident fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X, RADARSAT-2 and ALOS-2 satellites are investigated. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies and slightly different for L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected. Conference Object Sea ice German Aerospace Center: elib - DLR electronic library 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 338 341 |
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Open Polar |
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German Aerospace Center: elib - DLR electronic library |
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language |
English |
topic |
SAR-Signalverarbeitung |
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SAR-Signalverarbeitung Singha, Suman Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR |
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SAR-Signalverarbeitung |
description |
In recent years SAR Polarimetry has become a valuable tool in space-borne SAR based sea ice analysis. This work compares the polarimetric backscatter behavior of sea ice in space-borne X-band C-band and L-band Synthetic Aperture Radar (SAR) imagery. Two sets of spatially and temporally near coincident fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X, RADARSAT-2 and ALOS-2 satellites are investigated. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies and slightly different for L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected. |
format |
Conference Object |
author |
Singha, Suman |
author_facet |
Singha, Suman |
author_sort |
Singha, Suman |
title |
Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR |
title_short |
Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR |
title_full |
Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR |
title_fullStr |
Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR |
title_full_unstemmed |
Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR |
title_sort |
evaluation of polarimetric features for sea ice characterization at x, c and l- band sar |
publisher |
IEEE Xplore |
publishDate |
2017 |
url |
https://elib.dlr.de/110693/ https://elib.dlr.de/110693/2/SINGHA_IGARSS_2017.pdf https://doi.org/10.1109/IGARSS.2017.8126965 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://elib.dlr.de/110693/2/SINGHA_IGARSS_2017.pdf Singha, Suman (2017) Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 338-341. IEEE Xplore. IGARSS 2017, 2017-07-23 - 2017-07-28, Fort Worth, Texas, USA. doi:10.1109/IGARSS.2017.8126965 <https://doi.org/10.1109/IGARSS.2017.8126965>. ISSN 2153-7003. |
op_doi |
https://doi.org/10.1109/IGARSS.2017.8126965 |
container_title |
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
container_start_page |
338 |
op_container_end_page |
341 |
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1799488771756916736 |