Tensor Block Sparsity Based Representation for Spectral Spatial Hyperspectral Image Classification
Recently, 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.
Remote. Sens.