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We propose a multi-level semantics discovery approach for bridging the semantic gap when mining highresolution Polarimetric Synthetic Aperture Radar (PolSAR) remote sensing images. First, an Entropy/Anisotropy/Alpha-Wishart classifier is employed to discover low-level semantics as classes representing the physical scattering properties of targets (e.g., low-entropy/surface-scattering/high-anisotropy). Then, the images are tiled into patches and each patch is modeled as a Bag-of-Words (BoW), adoi:10.1109/lgrs.2016.2636663 fatcat:tjwmyyfrobaibb5sj7p65ma6mu