Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification
Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the...
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ftmdpi:oai:mdpi.com:/2072-4292/13/4/552/ 2023-08-20T04:09:43+02:00 Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking agris 2021-02-04 application/pdf https://doi.org/10.3390/rs13040552 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13040552 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 4; Pages: 552 classification sea ice ice types SAR Sentinel-1 texture GLCM incident angle Text 2021 ftmdpi https://doi.org/10.3390/rs13040552 2023-08-01T01:00:26Z Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice. Text Sea ice MDPI Open Access Publishing Remote Sensing 13 4 552 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
classification sea ice ice types SAR Sentinel-1 texture GLCM incident angle |
spellingShingle |
classification sea ice ice types SAR Sentinel-1 texture GLCM incident angle Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
topic_facet |
classification sea ice ice types SAR Sentinel-1 texture GLCM incident angle |
description |
Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice. |
format |
Text |
author |
Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking |
author_facet |
Johannes Lohse Anthony P. Doulgeris Wolfgang Dierking |
author_sort |
Johannes Lohse |
title |
Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
title_short |
Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
title_full |
Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
title_fullStr |
Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
title_full_unstemmed |
Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
title_sort |
incident angle dependence of sentinel-1 texture features for sea ice classification |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13040552 |
op_coverage |
agris |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing; Volume 13; Issue 4; Pages: 552 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13040552 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13040552 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
4 |
container_start_page |
552 |
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1774723371184947200 |