PROJECT TITLE :

Spectral–Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images

ABSTRACT:

Clustering for hyperspectral pictures (HSIs) is a very challenging task because of its inherent complexity. In this paper, we propose a novel spectral–spatial sparse subspace clustering $(textS^4textC)$ algorithm for hyperspectral remote sensing images. Initial, by treating each quite land-cowl class as a subspace, we introduce the sparse subspace clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial data of the HSIs are considered in the SSC model to obtain a a lot of correct coefficient matrix, which is employed to make the adjacent matrix. Finally, spectral clustering is applied to the adjacent matrix to get the ultimate clustering result. Several experiments were conducted to illustrate the performance of the proposed $textS^4textC$ algorithm.