An Accurate Subspace Clustering Model for Unsupervised Noise-laden Hyperspectral Image Segmentation

Abstract

The hyperspectral image acquisition in realistic situations is accompanied by a lot of noise, which dramatically interferes with the discriminative power of the clustering model. In this paper, a superpixel segmentation 3D regularized subspace clustering model (SS3D-SSC) is provided, which uses a superpixel segmentation method to obtain the most representative pixel in each tiny region and use it as a representative to replace other pixels in the same area, and then use sparse subspace clustering to perform the subsequent image segmentation, the proposed method in this paper is used in Indian Pines.The reliability of the algorithm is confirmed on the Indian Pines, Salinas and Pavia University datasets. Compared with the most advanced SSC-based algorithms, even using graph convolution subspace clustering, SS3D-SSC model has better classification performance and anti-noise ability.