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Manifold Sparse Coding Based Hyperspectral Image Classification
Yanbin Peng,Zhijun Zheng,Jiming Li,Zhigang Pan,Xiaoyong Li,Zhinian Zhai 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.12
Hyperspectral image classification has received an increasing amount of interest in recent years. However, when representing pixels as vectors, the dimensionality of feature space is high, which causes “curse of dimensionality” problem. In this paper, in order to alleviate the impact of above problem, a manifold sparse coding method is proposed. Firstly, matrix decomposition technique is used to find a concept set and calculates relative data projection in the concept set. Secondly, manifold learning regularization is imported into objective function to capture the intrinsic geometric structure in the data. Finally, LASSO regularization is used to obtain sparse representation of data projection. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.