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

arXiv: https://arxiv.org/abs/2608.15054 · HTML: https://mlsanity.com/paper/2608.15054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 60 references that are in the map.
- First posted on arXiv 15 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2403.05419/) (2024)
- [General Object Foundation Model for Images and Videos at Scale](https://mlsanity.com/paper/2312.09158/) (2023)
- [Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images](https://mlsanity.com/paper/2009.02130/) (2020)
- [A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2104.12137/) (2021)
- [Semantic Segmentation of Remote Sensing Images with Sparse Annotations](https://mlsanity.com/paper/2101.03492/) (2021)
- [I-MedSAM: Implicit Medical Image Segmentation with Segment Anything](https://mlsanity.com/paper/2311.17081/) (2023)
- [Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection](https://mlsanity.com/paper/2407.12339/) (2024)
- [SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection](https://mlsanity.com/paper/2309.01907/) (2023)
- [Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2412.19492/) (2024)
- [SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation](https://mlsanity.com/paper/2405.01992/) (2024)
- [ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object](https://mlsanity.com/paper/2503.12006/) (2025)
- [SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention](https://mlsanity.com/paper/2406.05802/) (2024)
- [Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2504.02647/) (2025)
- [AlertTrap: A study on object detection in remote insects trap monitoring system using on-the-edge deep learning platform](https://mlsanity.com/paper/2112.13341/) (2021)

## Abstract (arXiv)

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, 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 multi-scale 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 codes are available at: https://github.com/oucailab/FE-SAM.