Spatial–Spectral Feature-Enhanced Mamba and SAM-Guided Hyperspectral Multiclass Change Detection

Multi-class change detection from hyperspectral image (HSI) leverages the rich spectral information of HSIs to detect and classify subtle changes of interest in an imaged scene. However, challenges arise due to limited samples in small categories, which hinder the accurate differentiation of changes. This study proposes a spatial-spectral feature-enhanced Mamba and SAM-guided hyperspectral multi-class change detection (SFMS) method. To address the challenges, a tri-plane gated Mamba is designed to complement spatial information by utilizing the abundant spectral information in HSIs. Additionally, frequency domain features are combined with state space models, enabling the detection of more accurate semantic and texture changes using integrated information from frequency domains. This approach effectively mitigates the problem of inaccurate detection in small-sample categories. Furthermore, the segment anything model (SAM) is adapted, with the features of change areas being enhanced through prior knowledge obtained from segmentation, thereby improving the multi-class change detection accuracy. The experimental results demonstrate that the proposed SFMS method outperforms state-of-the-art techniques, achieving superior multi-class change detection while overcoming the challenges associated with detecting small-sample categories.