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- W2806985227 abstract "Sparsity-based classification methods have been widely used in hyperspectral image (HSI) classification. These methods typically assumed Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust super-pixel level joint sparse representation classification model (RSJSRC) to address the mixed noise problem in sparsity-based HSI classification. Our method takes into account both Gaussian and sparse noise. Experimental results on simulated and real data demonstrate the efficiency of the proposed method and clear benefits from the introduced mixed-noise model." @default.
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- W2806985227 date "2017-01-01" @default.
- W2806985227 modified "2023-09-23" @default.
- W2806985227 title "Robust joint sparsity model for hyperspectral image classification" @default.
- W2806985227 hasPublicationYear "2017" @default.
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