Frequency-and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation

Abstract

Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land-cover information. The segment anything model (SAM), proposed as a foundation vision model, offers strong segmentation performance and generalization capabilities for RSISS tasks. However, existing SAM-based approaches face two limitations: 1) insufficient adaptation of SAM’s features to the diverse characteristics of land-cover types; and 2) semantic ambiguity at object boundaries, which hinders accurate delineation. To address these limitations, we propose the frequency- and edge-guided SAM (FE-SAM), a scalable and efficient framework for RSISS. In particular, we introduce a frequency-modulated adapter (FMA) that adaptively decomposes and modulates frequency-domain features based on the input data. It selectively enhances informative high- and low-frequency components corresponding to different land-cover types. Furthermore, to improve SAM’s ability to capture fine-grained details, we design EGRefiner, which integrates multiscale edge-enhanced information extracted from the input image. Extensive experiments on three benchmark datasets demonstrate that FE-SAM outperforms state-of-the-art methods. The source code is available at: https://github.com/oucailab/FE-SAM