解耦与校正：面向开放词汇遥感分割的语义保持结构增强
Decouple and Rectify: Semantics-Preserving Structural Enhancement for Open-Vocabulary Remote Sensing Segmentation
AI总结：
本文提出DR-Seg框架，通过解耦CLIP特征为语义主导和结构主导子空间，并结合图校正模块和不确定性引导的融合模块，提升遥感开放词汇分割的精度与结构细节捕捉能力。
摘要：
开放词汇语义分割在遥感领域需要语言对齐的识别和细粒度空间界定。尽管CLIP提供稳健的语义泛化，但其全局对齐的视觉表示难以捕捉结构细节。近期方法通过引入遥感预训练DINO特征来补偿，但无法定位需要结构增强的区域，导致边界界定不明确且破坏CLIP语义完整性。为此，本文提出DR-Seg框架，通过解耦CLIP特征为语义主导和结构主导子空间，利用DINO进行针对性结构增强，随后通过图校正模块注入高保真结构先验，结合不确定性引导的融合模块动态整合，最终实现更精确的分割。
英文摘要：
Open-vocabulary semantic segmentation in the remote sensing (RS) field requires both language-aligned recognition and fine-grained spatial delineation. Although CLIP offers robust semantic generalization, its global-aligned visual representations inherently struggle to capture structural details. Recent methods attempt to compensate for this by introducing RS-pretrained DINO features. However, these methods treat CLIP representations as a monolithic semantic space and cannot localize where structural enhancement is required, failing to effectively delineate boundaries while risking the disruption of CLIP's semantic integrity. To address this limitation, we propose DR-Seg, a novel decouple-and-rectify framework in this paper. Our method is motivated by the key observation that CLIP feature channels exhibit distinct functional heterogeneity rather than forming a uniform semantic space. Building on this insight, DR-Seg decouples CLIP features into semantics-dominated and structure-dominated subspaces, enabling targeted structural enhancement by DINO without distorting language-aligned semantics. Subsequently, a prior-driven graph rectification module injects high-fidelity structural priors under DINO guidance to form a refined branch, while an uncertainty-guided adaptive fusion module dynamically integrates this refined branch with the original CLIP branch for final prediction. Comprehensive experiments across eight benchmarks demonstrate that DR-Seg establishes a new state-of-the-art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
