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Multi-View Feature Selection for PolSAR Image Classification via l₂,₁ Sparsity Regularization and Manifold Regularization | IEEE Journals & Magazine | IEEE Xplore

Multi-View Feature Selection for PolSAR Image Classification via l₂,₁ Sparsity Regularization and Manifold Regularization


Abstract:

Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) genera...Show More

Abstract:

Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) generated from the PolSAR data and various polarimetric target decompositions, texture features (TF) of the Pauli color-coded PolSAR images are used as features for PolSAR image classification. The obtained PF and TF often form the high-dimensional data, which leads to high computational complexity. Moreover, some features are irrelative and do nothing to improve the classification performance. Therefore, it is fairly indispensable to select a subset of useful features for PolSAR image classification. This paper proposes a multi-view feature selection method for PolSAR image classification. Firstly, two types of features, PF and TF are generated separately. Then the optimization model is built to pursue the feature selection matrices. Specifically, in order to maintain the consistency of different types of features, we search for the common representation of multiple types of features in the optimization problem. The l_{2,1} norm sparsity regularization is imposed on the feature selection matrices to achieve feature selection. In addition, the manifold regularization on the common representation is utilized to preserve the structure information of the data. The effectiveness of the proposed method is evaluated on three real PolSAR data sets. Experimental results demonstrate the superiority of the proposed method.
Published in: IEEE Transactions on Image Processing ( Volume: 30)
Page(s): 8607 - 8618
Date of Publication: 14 October 2021

ISSN Information:

PubMed ID: 34648443

Funding Agency:


References

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