Targeted Incorporating Spatial Information in Sparse Subspace Clustering of Hyperspectral Remote Sensing Images

Abstract

Methods based on sparse subspace clustering (SSC) have shown great potential for hyperspectral image (HSI) clustering. However their performance is limited due to the complex spatial-spectral structure in HSIs. In this paper, a spatial best-fit direction (SBFD) algorithm is proposed to update the coefficients obtained from sparse representation to more discriminant features by integrating the spatial-contextual information given by the best-fit pixel of each target pixel. Also, SBFD is more targeted by searching for the best-fit direction than directly using the local window to do max pooling. The proposed SBFD was tested on two widely used hyperspectral dataset, the experimental results indicate its improvement in the clustering accuracy and spatial homogeneity.