Hyperspectral image segmentation with low-rank representation and spectral clustering
Hyperspectral image segmentation is considered in this paper. A low-rank representation method is applied first to extract major data information, followed by a spectral clustering method based on data graph. Segmentation accuracy can be further improved by a post-processing step using non-local majority voting. Experimental results demonstrate that the proposed method can improve segmentation accuracy for complicated image scenes.