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Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

Permanent lenke
https://hdl.handle.net/10037/20605
DOI
https://doi.org/10.3390/rs13040552
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article.pdf (1.700Mb)
Publisert versjon (PDF)
Dato
2021-02-04
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Lohse, Johannes; Doulgeris, Anthony Paul; Dierking, Wolfgang Fritz Otto
Sammendrag
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.
Er en del av
Lohse, J.P. (2021). On Automated Classification of Sea Ice Types in SAR Imagery. (Doctoral thesis). https://hdl.handle.net/10037/20606.
Forlag
MDPI
Sitering
Lohse, Doulgeris, Dierking. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification. Remote Sensing. 2021
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  • Artikler, rapporter og annet (fysikk og teknologi) [1057]
Copyright 2021 The Author(s)

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