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• W4285594672 abstract "Polarimetric synthetic aperture radar (POLSAR) systems provide high resolution images containing polarimetric information. So, they have high capability in land cover classification. In this work, a binary coding-based polarimetric-morphological (BCPM) feature extraction is proposed for POLSAR image classification. At first, a set of polarimetric features is proposed. Then, a new morphological framework is introduced for contextual feature extraction from the POLSAR cube. The coherence matrix is composed from diagonal and non-diagonal elements with different information. These elements are analysed separately in the proposed method. Moreover, the amplitude and phase components of the non-diagonal elements are individually analysed using morphological filters by reconstruction. Finally, a binary coding-based polarimetric-spatial feature reduction, which uses the first order statistics, is proposed for feature transformation. The experiments on three real POLSAR images and a synthetic dataset show the superior performance of BCPM compared to several classification methods." @default.
• W4285594672 created "2022-07-16" @default.
• W4285594672 creator A5041239744 @default.
• W4285594672 date "2022-07-14" @default.
• W4285594672 modified "2023-09-23" @default.
• W4285594672 title "Polarimetric SAR image classification using binary coding‐based polarimetric‐morphological features" @default.
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• W4285594672 doi "https://doi.org/10.1049/ipr2.12587" @default.
• W4285594672 hasPublicationYear "2022" @default.
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