Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery

Hyperspectral image (HSI) Clustering is an unsupervised task, which segments pixels into different groups without using labeled samples. In this paper, spatial-spectral based multi-view low-rank sparse subspace clustering (SSMLC) algorithm is proposed. Due to significant number of spectra bands HSI contains much more information than a regular image. These spectral information can be considered as multiview. In this paper, the spectral partitioning is applied to generate spectral views which contain correlated bands. Morphological features of the original HSI are taken as another view which contains spatial features. Principal components construct another view, which eliminates the noise in the original dataset. After the multi-view dataset is formed, multi-view low-rank sparse subspace clustering is applied to segment HSI. Our experiments show that the performance of the proposed SSMLC is better than other that of clustering algorithms such as sparse subspace clustering and low-rank sparse subspace clustering.