Hypergraph Self-Supervised Learning based Joint Spectral-Spatial-Temporal Feature Representation for Hyperspectral Image Change Detection

Deep learning has shown promising performance in the field of hyperspectral image (HSI) change detection (CD). However, most of these methods often focus on local spatial-spectral information but ignore the high-order correlations contained in multi-temporal HSIs. To address this issue, this article proposes a hypergraph self-supervised learning (HG-SSL) based joint spectral-spatial-temporal feature representation algorithm (HyperSST) for downstream HSI-CD. Inspired by the process of human brain perception, HyperSST uniformly models the spectral-spatial-temporal correlations in the form of high-order interactions and skillfully exploits the vertex-level, hyperedge-level, and vertex/hyperedge-level inherent structures within the unlabeled multi-temporal HSIs. Specifically, two types of hyperedges, spectral-spatial correlation hyperedge and temporal correlation hyperedge, are firstly formulated to fully exploit the high-order spectral, spatial and temporal interactions contained in bi-temporal images. Secondly, two SSL strategies namely contrastive spectral-spatial features learning and generative temporal features learning are skillfully designed to exploit the inherent characteristics of hypergraph models and extract discriminative latent feature representations for downstream task. The former captures changes in both attribute space and structure space, while the latter apprehends the changes in temporal space. Thirdly, the learned joint spectral-spatial-temporal features provide a comprehensive representation to qualify the changes between multi-temporal images. Extensive experiments on four challenging HSI datasets demonstrate the effectiveness of the proposed approach Index Terms Hyperspectral Image Change detection (HSI-CD), self-supervised learning (SSL), hypergraph model, spectral-spatial-temporal feature representation, contrastive learning, generative learning.