Remote sensing change detection (RSCD) aims to identify the spatio-temporal changes of land cover, providing critical support for multi-disciplinary applications (e.g., environmental monitoring, disaster assessment, and climate change studies). Existing methods focus either on extracting features from localized patches, or pursue processing entire images holistically, which leads to the cross temporal feature matching deviation and exhibiting sensitivity to radiometric and geometric noise. Following the above issues, we propose a dual-module collaboration guided hierarchical adaptive aggregation framework, namely HA2F, which consists of dynamic hierarchical feature calibration module (DHFCM) and noise-adaptive feature refinement module (NAFRM). The former dynamically fuses adjacent-level features through perceptual feature selection, suppressing irrelevant discrepancies to address multi-temporal feature alignment deviations. The NAFRM utilizes the dual feature selection mechanism to highlight the change sensitive regions and generate spatial masks, suppressing the interference of irrelevant regions or shadows. Extensive experiments verify the effectiveness of the proposed HA2F, which achieves state-of-the-art performance on LEVIR-CD, WHU-CD, and SYSU-CD datasets, surpassing existing comparative methods in terms of both precision metrics and computational efficiency. In addition, ablation experiments show that DHFCM and NAFRM are effective. \href{https://huggingface.co/InPeerReview/RemoteSensingChangeDetection-RSCD.HA2F}{HA2F Official Code is Available Here!}

翻译：遥感变化检测旨在识别地表覆盖的时空变化，为环境监测、灾害评估和气候变化研究等多学科应用提供关键支持。现有方法或侧重于从局部图像块提取特征，或追求整体处理完整图像，这导致跨时相特征匹配偏差，并对辐射与几何噪声表现出敏感性。针对上述问题，本文提出一种双模块协作引导的层次化自适应聚合框架，即HA2F，其包含动态层次特征校准模块与噪声自适应特征优化模块。前者通过感知特征选择动态融合相邻层次特征，抑制无关差异以解决多时相特征对齐偏差。噪声自适应特征优化模块利用双重特征选择机制突出变化敏感区域并生成空间掩码，抑制无关区域或阴影的干扰。大量实验验证了所提HA2F的有效性，其在LEVIR-CD、WHU-CD和SYSU-CD数据集上取得了最先进的性能，在精度指标与计算效率方面均超越现有对比方法。此外，消融实验表明动态层次特征校准模块与噪声自适应特征优化模块均具有显著效果。\href{https://huggingface.co/InPeerReview/RemoteSensingChangeDetection-RSCD.HA2F}{HA2F官方代码已开源！}