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Adaptive Model-Based Classification of PolSAR Data
Li, Dong1; Zhang, Yunhua1,2
KeywordRadar polarimetry scattering model scattering similarity target decomposition unsupervised classification
AbstractAn adaptive classification is developed as a hybrid of the eigenvector-based and the model-based target decompositions for polarimetric synthetic aperture radar (PolSAR) data. The classification adopts the canonical scattering models that widely used in model-based decompositions to provide an improvement for the well-known H/alpha classification. First, a correspondence principle is adopted to adaptively identify the matched canonical models. The selected models are parallelly combined based on the scattering similarity for a fine depiction of the scattering mechanism then. Twelve classes are finally obtained, and each one carries a unique symbol to show a specific scattering. The classification does not depend on a particular data set, avoids the hard partitioning, and solves the obscures in H/alpha. Comparison on the real PolSAR data sets with H/alpha and the existing scattering similarity-based classification validates the better discrimination. ]
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Document Type期刊论文
Affiliation1.Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing; 100190, China;
2.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing; 10049, China
Recommended Citation
GB/T 7714
Li, Dong,Zhang, Yunhua. Adaptive Model-Based Classification of PolSAR Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(12):6940-6955.
APA Li, Dong,&Zhang, Yunhua.(2018).Adaptive Model-Based Classification of PolSAR Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(12),6940-6955.
MLA Li, Dong,et al."Adaptive Model-Based Classification of PolSAR Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.12(2018):6940-6955.
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