通过频域调制与边缘增强，提升遥感图像语义分割精度
Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation
- 引入频域调制适配器，动态优化不同地物的高低频特征
- 在三个基准数据集上达到领先性能，边界分割更准确
- 适合遥感影像精细分类任务，代码已开源
遥感图像语义分割（RSISS）因对细粒度地表覆盖信息的需求而备受关注。基于通用视觉模型的分割一切模型（SAM）在该任务中展现出强大的分割性能和泛化能力。然而现有基于SAM的方法存在两大局限：(1) SAM特征对多样地物类型的适应性不足；(2) 物体边界存在语义模糊，影响精确勾勒。为此，本文提出频域与边缘引导的SAM（FE-SAM），一个可扩展且高效的遥感图像语义分割框架。具体而言，提出频域调制适配器（FMA），根据输入数据自适应分解并调制频域特征，选择性增强对应不同地物类型的信息性高低频成分。此外，为提升捕捉细粒度细节的能力，设计了EGRefiner模块，融合从输入图像中提取的多尺度边缘增强信息。在三个基准数据集上的大量实验表明，FE-SAM优于现有最先进方法。源代码已公开：https://github.com/oucailab/FE-SAM。
原文摘要 · Abstract (English)
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.
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