Graph-Based Structural Deep Spectral-Spatial Clustering for Hyperspectral Image
Bo Peng, Yuxuan Yao, Jianjun Lei, Leyuan Fang, Qingming Huang
Abstract
As a fundamental task in hyperspectral image (HSI) applications, HSI clustering has been extensively researched over the years. Inspired by the capability of graphs in structure modeling, some recent works have exploited the local graph-structured information among pixels to boost the HSI clustering performance. However, these works only explore shallow graph structures for representation learning, while ignoring the beneficial high-order as well as global structure characteristics of HSI pixels, thus resulting in limited clustering performance. In this paper, a novel graph-based structural deep spectral-spatial clustering network (GSDSSC) is proposed to sufficiently explore the structure information among pixels. Specifically, a self-expression embedded multi-graph auto-encoder is proposed to explore high-order structure associations among pixels, thereby capturing robust spectral-spatial features and global clustering structure. Besides, to learn more compact spectral-spatial features for clustering, a global structure-guided optimization mechanism is further designed to constrain the local feature distribution with global clustering structure. Extensive experiments on three public HSI benchmark datasets illustrate the superior performance of the proposed GSDSSC over state-of-the-art HSI clustering methods.