Keywordsship classification; improved shape contexts; scattering centers; synthetic aperture radar (SAR) image; SAR; ocean; NRCS; Doppler spectrum; azimuthal ambiguity; directional spreading function; gravity-capillary wave; radar observations; synthetic aperture radar (SAR); high-resolution and wide-swath (HRWS); velocity estimation; Doppler ambiguities; maximum likelihood (ML); NRCS; extreme wind conditions; low incidence angles; radar backscatter; modulation model; internal wave; third-order statistics; high frequency band radar; shallow water; swell waves; water depth; dispersion relationship; quad-polarization; Bragg scattering; local gradients method; retrieval; SAR; wind direction; GF-3; synthetic aperture radar (SAR); ocean surface wind; validation; SAR; significant wave height; co-polarization; TerraSAR-X/TanDEM-X; moving target imaging; parametric sparse representation (PSR); region of interest (ROI); synthetic aperture radar (SAR); SAR; sea surface wind; assimilation; observational error; typhoon; cross-pol; Sentinel-1; radar backscattering; wind retrieval; context information; convolutional neural network (CNN); ship detection; synthetic aperture radar (SAR); Sentinel-1; azimuth ambiguities; littoral zones; multi-temporal; RGB composition; synthetic aperture radar (SAR); Sentinel-1; Caspian Sea; Black Sea; internal waves; satellite remote sensing; sea surface; SAR imagery.; InSAR; near-nadir incidence; Ku-band; ocean surface topography; altimetry; tropical cyclone; scatterometer wind; SAR wind; data assimilation; quality control; Huber norm; oil spill detection; SAR; active learning; active sample selecting; support vector machine; k-nearest neighbor; linear discriminant analysis; decision tree; bivalve beds; intertidal flats; Kennaugh elements; SAR; dual-polarization; multi-frequency; SAR; Subei Bank; shallow water topography
