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| Spatial Correlation Constrained Sparse Representation for Hyperspectral Image Classification |
| Liu Jian-jun Wu Ze-bin Wei Zhi-hui Xiao Liang Sun Le |
| School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract A novel classification method of hyperspectral image based on sparse representation is proposed. First, the training data is used to design a structured dictionary, and a classification model of hyperspectral image is built based on sparse representation; Then the spatial correlation and the spatial information of training data are added to improve the accuracy of this model; Finally it is solved by the rapid alternating direction method of multipliers. The experimental results show that the proposed method can improve the classification results, and the results are stable.
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Received: 14 May 2012
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Corresponding Authors:
Wu Ze-bin
E-mail: fen_jin@163.com
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2012, Vol. 34 
