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 environmental applications. Previous studies have investigated the class-dependent decrease SAR backscatter intensity with incident angle (IA); others shown potential textural information to improve automated image classification. In this work, we investigate inclusion Sentinel-1 (S1) texture features into a Bayesian classifier that accounts linear per-class variation its IA. We use S1 extra-wide swath (EW) product ground-range detected format at medium resolution (GRDM), compute seven grey level co-occurrence matrix (GLCM) from HH HV logarithmic domain. While GLCM obtained domain vary significantly IA, computed do not depend on IA or reveal only weak, approximately dependency. They can therefore be directly included IA-sensitive assumes variation. The different number looks first sub-swath (EW1) causes distinct offset boundary between EW1 second (EW2). This must considered when using classification; demonstrate manual correction example contrast. Based Jeffries–Matusita distance class histograms, perform separability analysis 57 parameter settings. select suitable combination classes our data set classify several test features. compare results intensity. Particular improvements are achieved generalized separation water, as well young multi-year ice.