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Hermitian positive definite (HPD) covariance matrices form one of the most widely-used data representations in PolSAR applications. However, most of these applications either use statistical distribution models on the PolSAR covariance matrices or polarimetric target decomposition. In this paper, we study HPD matrices for PolSAR image classification in the context of sparse coding. More specifically,...
A novel Polarimetric-Texture-Structure descriptor for high-resolution PolSAR image is presented in this paper. More precisely, a PolSAR image is represented by a tree of shapes, each of which is associated with several polarimetric and texture attributes. We first extract the texture properties and polarimetric characteristics from each shape, then use the shape co-occurrence patterns (SCOPs) to characterize...
Lots of SAR polarimetric features have been proposed to discriminate the different scattering processes of earth terrain. Using the full set of these features for classification is computationally too expensive and some of the features may be irrelevant to the classification task and other may be redundant. Thus, it is useful to exploit the discriminative power offered by a selection and combination...
PolSAR image segmentation has long been an important problem in the PolSAR remote sensing community. Many segmentation algorithms describe images in terms of a hierarchy of regions has attracted particular attention in recent years. However, they often contain more data than is required for an efficient description. In this paper, we propose an effective measure to extract hierarchical semantic structures...
In this paper, we present a study of extracting urban areas from Polarimetric Synthetic Aperture Radar (PolSAR) images using only positive samples. We solve this problem by learning a standard binary classifier (urban/non-urban) given an incomplete set of positive samples (urban) and a set of unlabeled samples (some of which are urban and some of which are non-urban) based on the work of Elkan and...
The paper proposes a fast and accurate semantic segmentation approach for a large Polarimetric SAR (PolSAR) image using Conditional Random Fields (CRFs). It efficiently incorporates the polarimetric signatures, texture and intensity features into a unite CRFs model, and employs a fast max-margin training method for parameters learning. Experiments on RadarSat-2 PolSAR data in Flevoland test site demonstrate...
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