Name
Title | ||
|---|---|---|
Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/rs8080636 | REMOTE SENSING |
Keywords | Field | DocType |
hyperspectral image (HSI),classification,tensor,dictionary learning,sparse representation | Residual,Computer vision,Dictionary learning,Pattern recognition,Tensor,Matrix (mathematics),Sparse approximation,Hyperspectral imaging,Artificial intelligence,Discriminative model,Mathematics,Cube | Journal |
Volume | Issue | ISSN |
8 | 8 | 2072-4292 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||