Journal
PATTERN RECOGNITION
Volume 60, Issue -, Pages 1041-1056Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2016.04.009
Keywords
Classification; Hyperspectral image (HSI); Dictionary learning; Sparse representation; Low-rank representation
Funding
- National High Technology Research and Development Program of China (863 Program) [2013AA122302]
- National Natural Science Foundation of China [41501368, 41522104, 41531178]
- Fundamental Research Funds for the Central Universities [16lgpy04, 15lgjc24]
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Previous studies have demonstrated that the structured sparse representation can yield significant improvements in spectral-spatial hyperspectral classification. However, a dictionary that contains all of the training samples in the sparsity-aware methods is ineffective in capturing the class-discriminative information. This paper makes the first attempt to learn group-based sparse and low-rank representation for improving the dictionary. First, super-pixel segmentation is applied to obtain homogeneous regions that act as spatial groups. Dictionary is then learned with group-based sparse and low-rank regularizations to achieve common representation matrix for the same spatial group. Those group-based sparse and low-rank regularizations facilitate identifying both local and global structure of the hyperspectral image (HSI). Finally, representation matrices of test samples are employed to determine the class labels by a linear support vector machine (SVM). Experimental results on two benchmark HSIs show that the proposed method achieves better performance than the state-of-the-art methods, even with small sample sizes. (C) 2016 Elsevier Ltd. All rights reserved.
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