Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification
1
Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Tromsø, Norway
2
Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bussestr. 24, 27570 Bremerhaven, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Juha Karvonen
Remote Sens. 2021, 13(4), 552; https://doi.org/10.3390/rs13040552
Received: 24 December 2020 / Revised: 22 January 2021 / Accepted: 1 February 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)
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.
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Keywords:
classification; sea ice; ice types; SAR; Sentinel-1; texture; GLCM; incident angle
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Lohse, J.; Doulgeris, A.P.; Dierking, W. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sens. 2021, 13, 552. https://doi.org/10.3390/rs13040552
AMA Style
Lohse J, Doulgeris AP, Dierking W. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sensing. 2021; 13(4):552. https://doi.org/10.3390/rs13040552
Chicago/Turabian StyleLohse, Johannes; Doulgeris, Anthony P.; Dierking, Wolfgang. 2021. "Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification" Remote Sens. 13, no. 4: 552. https://doi.org/10.3390/rs13040552
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