Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 55, Issue 12, Pages 6683-6694Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2017.2727067
Keywords
Multilayer projective dictionary pair learning (MDPL); polarimetric synthetic aperture radar (PolSAR); sparse autoencoder (SAE); sparse representation
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Funding
- National Natural Science Foundation of China [61571342, 61501353, 61272279]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
- Program for New Century Excellent Talents in University [NCET-12-0920]
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Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse autoencoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification.
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