Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification
AbstractRecently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC. View Full-Text
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He, Z.; Li, J.; Liu, L. Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification. Remote Sens. 2016, 8, 636.
He Z, Li J, Liu L. Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification. Remote Sensing. 2016; 8(8):636.Chicago/Turabian Style
He, Zhi; Li, Jun; Liu, Lin. 2016. "Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification." Remote Sens. 8, no. 8: 636.
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