Global Difference-Aware Mamba for Hyperspectral Image Change Detection

Deep-learning-based methods have been widely applied to hyperspectral image change detection (HSI-CD), where most existing approaches adopt a patchwise framework by taking paired patches from bitemporal HSIs as input and determining whether the central position has changed. However, these methods suffer from several limitations. Being patchwise, they fail to fully exploit the rich information contained in the entire bitemporal HSIs and often overlook useful content outside the training samples. Although some methods incorporate advanced modeling techniques, their receptive fields are still constrained by the patch size, which limits their ability to capture global dependencies. Besides, many approaches underuse the inherent advantage of HSI in capturing subtle spectral differences. To address these challenges, we propose an image-level global difference-aware Mamba (GDAMamba) for HSI-CD, consisting of three key modules: the global contextual difference awareness module (GCDAM), the temporal spectral difference enhancement module (TSDEM), and the adaptive gated fusion module (AGFM). Specifically, GCDAM enables global spatial contextual awareness by establishing associations between each pixel and all others on the difference map. TSDEM processes the bitemporal HSIs in a pixelwise manner, progressively extracts, and enhances spectral differences. These differences are then integrated into the global difference representations through AGFM, which are subsequently fed into a segmentation head to generate the final CD map. Experimental results on three benchmark datasets demonstrate that GDAMamba outperforms several state-of-the-art methods. Our code is available at https://github.com/Preston-Dong/GDAMamba