Bipartite Graph-Based Projected Clustering With Local Region Guidance for Hyperspectral Imagery

Abstract

Hyperspectral image (HSI) clustering is challenging to divide all pixels into different clusters because of the absent labels, large spectral variability and complex spatial distribution. Anchor strategy provides an attractive solution to the computational bottleneck of graph-based clustering for large HSIs. However, most existing methods require separated learning procedures and ignore noisy as well as spatial information. In this paper, we propose a bipartite graph-based projected clustering (BGPC) method with local region guidance for HSI data. To take full advantage of spatial information, HSI denoising to alleviate noise interference and anchor initialization to construct bipartite graph are conducted within each generated superpixel. With the denoised pixels and initial anchors, projection learning and structured bipartite graph learning are simultaneously performed in a one-step learning model with connectivity constraint to directly provide clustering results. An alternating optimization algorithm is devised to solve the formulated model. The advantage of BGPC is the joint learning of projection and bipartite graph with local region guidance to exploit spatial information and linear time complexity to lessen computational burden. Extensive experiments demonstrate the superiority of the proposed BGPC over the state-of-the-art HSI clustering methods.