Spectral-spatial clustering of hyperspectral remote sensing image with sparse subspace clustering model

Clustering for hyperspectral imagery (HSI) is a very challenging task due to its inherent spectral and spatial complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering (S ⁴ C) algorithm for hyperspectral imagery. Firstly, by treating each kind of ground class as a subspace, we introduce sparse subspace clustering (SSC) algorithm to HSIs. Then considering the spectral and spatial property of HSI, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the sparse subspace clustering model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Lastly, spectral clustering is applied to the adjacent matrix to obtain the final image clustering result. Several experiments were conducted to illustrate the performance of the proposed algorithm.