Water-Adapter: adapting the segment anything model for surface water extraction in optical very-high-resolution remotely sensed imagery

Surface water extraction (SWE) from very-high-resolution optical remote sensing images is crucial yet challenging due to the complex spectral variability of water bodies. To address this, we propose Water-Adapter, a novel method that enhances SWE by leveraging the Segment Anything Model (SAM), a large-scale image segmentation framework. Our approach introduces a task-specific input module that utilizes explicit visual prompting by focusing the tunable parameters on the visual content of each image, specifically leveraging features from frozen patch embeddings and low-frequency components. By freezing most of SAM's image encoder parameters and incorporating a few domain-specific trainable adapters, Water-Adapter effectively integrates remote sensing knowledge into SAM, significantly improving segmentation performance with minimal computational overhead. Extensive experiments on the GLH-Water dataset demonstrate that Water-Adapter outperforms state-of-the-art methods in both accuracy and efficiency.