发表机构
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对遥感变化检测模型域增量学习易灾难性遗忘问题，提出DG-FDD框架，集成差异引导自适应和频率解耦蒸馏，有效减轻遗忘，实验表明其在跨域变化检测中能平衡历史知识保留与新域适应，性能优于单任务模型。
AI 中文摘要
遥感变化检测（RSCD）模型在增量适应新域时容易出现灾难性遗忘。现有域增量学习（DIL）方法主要保留图像级表示，但往往忽略双时相差异线索，这对域转移下的稳健变化检测至关重要。为解决此限制，我们提出DG-FDD，一个集成差异引导自适应和频率解耦蒸馏的域增量变化检测框架。具体而言，差异引导动态适配器（DGDA）对双时相特征差异建模，以促进变化感知特征自适应并减少特定域干扰。同时，具有跨域合成的频率解耦知识蒸馏策略（FDKD-CS）在频域中将结构信息与域风格分离，实现无历史数据的稳定知识转移。在两个和三个域增量协议下对三个公共高分辨率RSCD数据集进行的大量实验表明，DG-FDD有效减轻了灾难性遗忘。与独立训练的单任务模型相比，DG-FDD在六个双域序列中F1和IoU的平均相对变化分别仅为-0.23%和-0.45%，在三个评估的三域序列中分别为-0.69%和-1.31%。这些结果表明在连续跨域变化检测中，历史知识保留和新域适应之间具有良好的稳定性-可塑性平衡。
英文摘要
Remote sensing change detection (RSCD) models are prone to catastrophic forgetting when incrementally adapted to new domains. Existing domain-incremental learning (DIL) methods mainly preserve image-level representations but often overlook bitemporal discrepancy cues, which are critical for robust change detection under domain shifts. To address this limitation, we propose DG-FDD, a domain-incremental change detection framework that integrates Difference-Guided Adaptation and Frequency-Decoupled Distillation. Specifically, the Difference-Guided Dynamic Adapter (DGDA) models bitemporal feature discrepancies to promote change-aware feature adaptation and reduce domain-specific interference. Meanwhile, the Frequency-Decoupled Knowledge Distillation strategy with Cross-domain Synthesis (FDKD-CS) separates structural information from domain style in the frequency domain, enabling stable knowledge transfer without historical data. Extensive experiments on three public high-resolution RSCD datasets under two- and three-domain incremental protocols demonstrate that DG-FDD effectively mitigates catastrophic forgetting. Compared with independently trained single-task models, DG-FDD records mean relative changes in F1 and IoU of only -0.23% and -0.45%, respectively, across six two-domain sequences, and -0.69% and -1.31%, respectively, across the three evaluated three-domain sequences. These results indicate a favorable stability-plasticity balance between historical knowledge retention and new-domain adaptation in continual cross-domain change detection.
Comments33 pages, 14 figures, and 5 tables
