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Keywordsoil spill; SAR data; compact polarimetric mode; image classification; feature selection; radar polarimetry; synthetic aperture radar (SAR); dual circular polarimetry (DCP); compact polarimetry; rice phenology; polarimetric interferometric synthetic aperture radar (PolInSAR); vegetation height; truncated singular value decomposition (TSVD); least squares; polarimetric SAR images; deep convolution neural network; dual-branch convolution neural network; land cover classification; land cover; supervised classification; texture measures; synthetic aperture radar (SAR) imagery; support vector machine; maximum likelihood; Tehran; PolSAR; dual polarimetry; quad polarimetry; decomposition; TerraSAR-X; Radarsat-2; ALOS; ALOS-2; tundra; arctic; polarimetry; SAR; precision agriculture; rice monitoring; stochastic optimization; metamodels; radiative transfer models; electromagnetic scattering models; PolSAR; image classification; composite kernel; polarimetric features; spatial features; feature fusion; aboveground biomass; tropical forest; microwave sensor system; n/a
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