面向遥感影像语义分割的频率与边缘引导型 Segment Anything 模型

中文摘要 AI 辅助

遥感影像语义分割（RSISS）因对细粒度土地覆盖信息的需求增长而受到广泛关注。作为基础视觉模型提出的 Segment Anything Model（SAM），在 RSISS 任务中展现出强大的分割性能与泛化能力，但现有基于 SAM 的方法存在两项局限：（1）SAM 的特征对各类土地覆盖类型的多样特性适配不足；（2）目标边界存在语义歧义，阻碍了精确的轮廓勾勒。为解决这些局限，本文提出面向 RSISS 的可扩展高效框架——频率与边缘引导型 SAM（FE-SAM）。具体而言，我们引入频率调制适配器（FMA），该适配器可基于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土地覆盖类型的有信息高频与低频分量。此外，为提升 SAM 捕获细粒度细节的能力，我们设计了 EGRefiner，该模块整合了从输入影像中提取的多尺度边缘增强信息。在三个基准数据集上开展的大量实验表明，FE-SAM 的性能优于当前最优方法。源代码可在：this https URL 获取。

英文摘要

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.

发表机构

• Ocean University of China（中国海洋大学）
• Mississippi State University（密西西比州立大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted for publication in IEEE TGRS 2026