Learning group-based sparse and low-rank representation for hyperspectral image classification

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.