CoSA：基于相关性的引导注意力与可学习残差门控用于遥感变化检测
CoSA: Correlation-Guided Change Attention with Learnable Residual Gating for Remote Sensing Change Detection
AI总结：
提出CoSA模块，利用双时特征相关性作为控制信号，通过可学习残差门控增强变化感知，在四个基准数据集上提升变化类F1达1.5-2.6%，参数开销极小。
摘要：
从双时相影像进行遥感变化检测（CD）对于城市监测、灾害评估和环境管理等应用至关重要，但在稀疏变化、噪声标签和外观变化下，鲁棒定位仍然具有挑战性。本文提出上下文采样注意力（CoSA），一种轻量级解码器侧精炼模块，明确利用双时特征相关性作为控制信号进行自适应变化感知特征增强。这不同于依赖隐式特征加权而无显式时间控制的传统注意力机制。在实现的FC-Siam设置中，CoSA计算配对解码器特征之间的归一化同位置互相关，将低相关性转换为变化门控，并通过可学习残差缩放将所得门控残差注入到原生1/8和1/16特征尺度。该设计能够有效区分稳定区域和模糊区域，而无需依赖计算昂贵的全局注意力。在四个基准数据集（LEVIR-CD、S2Looking、DSIFN和CLCD）上的大量实验表明，与强基线相比，该方法在变化类F1上获得了1.5-2.6%的持续提升，同时引入可忽略的参数开销。消融研究证实，多尺度放置和可学习残差门控对于达到峰值性能都很重要。这些结果表明，CoSA为增强孪生变化检测框架中的时间判别性建立了一种实用且有效的精炼范式。
英文摘要：
Remote sensing change detection (CD) from bi-temporal imagery is critical for applications such as urban monitoring, disaster assessment, and environmental management, yet robust localization remains challenging under sparse changes, noisy labels, and appearance variations. In this paper, we propose Context Sampling Attention (CoSA), a lightweight decoder-side refinement module that explicitly leverages bi-temporal feature correlation as a control signal for adaptive change-aware feature enhancement. This differs from conventional attention mechanisms that rely on implicit feature weighting without explicit temporal control. In the implemented FC-Siam setting, CoSA computes normalized same-location cross-correlation between paired decoder features, converts low correlation into a change gate, and injects the resulting gated residual at native 1/8 and 1/16 feature scales through learnable residual scaling. This design enables effective discrimination between stable and ambiguous regions without relying on computationally expensive global attention. Extensive experiments on four benchmark datasets (LEVIR-CD, S2Looking, DSIFN, and CLCD) demonstrate consistent improvements over strong baselines, achieving 1.5-2.6% gains in changed-class F1 while introducing negligible parameter overhead. Ablation studies confirm that multiscale placement and learnable residual gating are both important for peak performance. These results indicate that CoSA establishes a practical and effective refinement paradigm for enhancing temporal discriminability in Siamese change detection frameworks.
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。
