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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Online Access: | https://hdl.handle.net/10037/20605 https://doi.org/10.3390/rs13040552 |
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ftunivtroemsoe:oai:munin.uit.no:10037/20605 2023-05-15T14:27:10+02:00 Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification Lohse, Johannes Doulgeris, Anthony Paul Dierking, Wolfgang Fritz Otto 2021-02-04 https://hdl.handle.net/10037/20605 https://doi.org/10.3390/rs13040552 eng eng MDPI Lohse, J.P. (2021). On Automated Classification of Sea Ice Types in SAR Imagery. (Doctoral thesis). https://hdl.handle.net/10037/20606 . Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Lohse, Doulgeris, Dierking. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sensing. 2021 FRIDAID 1887117 doi:10.3390/rs13040552 2072-4292 https://hdl.handle.net/10037/20605 openAccess Copyright 2021 The Author(s) VDP::Technology: 500::Environmental engineering: 610 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.3390/rs13040552 2021-06-25T17:58:03Z 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. Article in Journal/Newspaper Arctic Sea ice University of Tromsø: Munin Open Research Archive Remote Sensing 13 4 552 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500::Environmental engineering: 610 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 |
spellingShingle |
VDP::Technology: 500::Environmental engineering: 610 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 Lohse, Johannes Doulgeris, Anthony Paul Dierking, Wolfgang Fritz Otto Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification |
topic_facet |
VDP::Technology: 500::Environmental engineering: 610 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 |
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 |
Article in Journal/Newspaper |
author |
Lohse, Johannes Doulgeris, Anthony Paul Dierking, Wolfgang Fritz Otto |
author_facet |
Lohse, Johannes Doulgeris, Anthony Paul Dierking, Wolfgang Fritz Otto |
author_sort |
Lohse, Johannes |
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 |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/20605 https://doi.org/10.3390/rs13040552 |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
Lohse, J.P. (2021). On Automated Classification of Sea Ice Types in SAR Imagery. (Doctoral thesis). https://hdl.handle.net/10037/20606 . Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Lohse, Doulgeris, Dierking. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sensing. 2021 FRIDAID 1887117 doi:10.3390/rs13040552 2072-4292 https://hdl.handle.net/10037/20605 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.3390/rs13040552 |
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Remote Sensing |
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13 |
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4 |
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552 |
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