Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation
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. (2) Semantic ambiguity at object boundaries, which hinders accurate delineation. To address these limitations, we propose Frequency and Edge-guided SAM (FE-SAM), a scalable and efficient framework for RSISS. Specifically, w...
| Authors | Feng Gao, Zizhe Pan, Haoting Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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