Domain Information Mining and State-Guided Adaptation Network for Multispectral Image Segmentation

Segment anything model (SAM), as a prompt-based image segmentation foundation model, demonstrates strong task versatility and domain generalization (DG) capabilities, providing a new direction for solving cross-scene segmentation tasks. However, SAM still has limitations in multispectral cross-domain segmentation tasks, mainly reflected in: 1) insufficient information utilization, which is reflected in the neglect of nonvisible spectral information and the shift information contained in source domain (SD) samples and target domain (TD) samples; and 2) lack of cross-domain strategies, which leads to insufficient cross-domain adaptation (DA) ability in downstream tasks. To address these challenges, we combine the respective advantages of masked autoencoder (MAE) and cross-domain strategies, propose an improved SAM DA network structure called domain information mining and state-guided adaptation network (DSAnet), aiming to enhance SAM's performance in multispectral cross-domain segmentation tasks from both data and task levels. At the data level, DSAnet incorporates a style masking learning component, which randomly masks image features and replaces them with domain-specific learnable tokens, integrated with the image reconstruction task, to mine the style information and domain invariance of the image itself. At the task level, DSAnet introduces domain state learning and style-guided segmentation: domain state learning, through a state sequence modeling approach, designs specific state representations for SD and TD to capture interdomain differences, thereby reducing task shift. Meanwhile, the learned domain state information can be directly applied to the inference stage. Style prompt segmentation guides the segmentation training process of SD images with TD style prompts, improving SAM's adaptability in cross-domain multispectral segmentation downstream tasks. Extensive experiments on three multitemporal multispectral image (MSI) datasets demonstrate the superiority of the proposed method compared to state-of-the-art cross-domain strategies and SAM variant methods.