Global Spatial–Spectral and Frequency-Domain Mamba for Hyperspectral Change Detection

Abstract

Hyperspectral image plays an indispensable role in the field of change detection, yet its application still faces numerous challenges. On one hand, traditional attention mechanisms are often constructed based on local information, making them prone to overlooking long-range contextual relationships hidden within global information. On the other hand, fine-grained features are susceptible to interference from irrelevant changes. To address these issues, this article proposes a network framework named global spatial–spectral and frequency-domain mamba (GSSFDM) to enhance the accuracy and robustness of change detection in hyperspectral images. The proposed framework comprises three core modules. Global spectral–spatial attention module constructs a global spectral–spatial self-attention mechanism based on global information, effectively capturing long-range correlations between the spatial and spectral dimensions, thereby enhancing the model's ability to understand global information. Fine-grained change feature extraction module utilizes flow field estimation techniques to filter out irrelevant change features, building prior knowledge to enhance the model's ability to distinguish changes. Concurrently, it combines Fourier transform to separate phase detail changes, further enhancing the representation of fine-grained features, which aids in more precise localization of change boundaries. Local enhancement Mamba module combines the Mamba architecture with an effective channel attention mechanism, specifically designed to strengthen the capture and representation of fine-grained change features, significantly improving the model's performance in perceiving and recognizing subtle changes. Extensive experiments on three benchmark datasets demonstrate that the proposed GSSFDM consistently outperforms existing state-of-the-art methods.