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- W2555971276 abstract "In this study, we consider the capability of a single look high-resolution PoLSAR image for discriminating different surfaces in urban area. First, a basic framework is set up to extract different polarimetric descriptors from Sinclair ma- trix and coherency matrices, coherent and incoherent decomposition descriptors, and some discriminators. Then, we use a subset selection method (SSM) based on GA-MI (Genetic Algorithm - Mutual Information) and mRMR (minimum Re- dundancy Maximum Relevance) evaluation function to obtain an optimal subset of descriptors as input of the classification process ((1),(2),(3),(4)). To show the ef- ficiency of this approach, we perform a comparison between the results of SVM classification obtained from the optimal feature subset and from different target decompositions based on different scattering mechanism assumptions, including the Pauli (5), Huynen (6), Cloude (7), Holm - Barnes (8), H/A/Alpha (7), Freeman (9,10), VanZyl (11), Krogager (12), Yamaguchi (13), or Touzi (14) methods. Exper- imental results on the optimal subset of SSM based on GA-MI show that the pro- posed method provides a meaningful selection in regards of the different scatter- ing mechanisms and leads to better classification performance. Our experiments are performed on a single look PoLSAR image acquired by the airborne RAM- SES SAR sensor of ONERA over a suburban area." @default.
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- W2555971276 date "2014-01-01" @default.
- W2555971276 modified "2023-09-22" @default.
- W2555971276 title "Polarimetric SAR Image Classification on Urban Area using a Subset Selection Method" @default.
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