Deep learning (DL) approaches based on CNN-purely or Transformer networks have demonstrated promising results in bitemporal change detection (CD). However, their performance is limited by insufficient contextual information aggregation, as they struggle to fully capture the implicit contextual dependency relationships among feature maps at different levels. Additionally, researchers have utilized pre-trained denoising diffusion probabilistic models (DDPMs) for training lightweight CD classifiers. Nevertheless, training a DDPM to generate intricately detailed, multi-channel remote sensing images requires months of training time and a substantial volume of unlabeled remote sensing datasets, making it significantly more complex than generating a single-channel change map. To overcome these challenges, we propose a novel end-to-end DDPM-based model architecture called change-aware diffusion model (CADM), which can be trained using a limited annotated dataset quickly. Furthermore, we introduce dynamic difference conditional encoding to enhance step-wise regional attention in DDPM for bitemporal images in CD datasets. This method establishes state-adaptive conditions for each sampling step, emphasizing two main innovative points of our model: 1) its end-to-end nature and 2) difference conditional encoding. We evaluate CADM on four remote sensing CD tasks with different ground scenarios, including CDD, WHU, Levier, and GVLM. Experimental results demonstrate that CADM significantly outperforms state-of-the-art methods, indicating the generalization and effectiveness of the proposed model.

翻译：基于纯CNN或Transformer网络的深度学习方法在双时相变化检测中已展现出显著效果。然而，由于难以充分捕捉不同层级特征图之间的隐式上下文依赖关系，其性能受限于上下文信息聚合不足。此外，研究者已利用预训练的去噪扩散概率模型（DDPM）训练轻量化变化检测分类器。但训练DDPM生成细节丰富、多通道的遥感图像需要数月训练时间和大量未标注遥感数据集，其复杂度远高于生成单通道变化图。为克服这些挑战，我们提出一种新型端到端DDPM模型架构——变化感知扩散模型（CADM），该模型可利用少量标注数据集快速训练。同时，我们引入动态差异条件编码，以增强DDPM对变化数据集中双时相图像的逐区域步进注意力。该方法为每个采样步骤建立状态自适应条件，突出模型的两大创新点：1）端到端特性；2）差异条件编码。我们在包含不同地面场景的四个遥感变化检测任务（CDD、WHU、Levier、GVLM）上评估了CADM。实验结果表明，CADM显著优于现有最优方法，验证了所提模型的泛化性与有效性。