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.

本研究提出了基于 DDPM 的 change-aware diffusion model (CADM) 模型，使用有限的标注数据快速训练，采用动态差分条件编码来增强 bitemporal change detection 任务中的区域注意力，实验结果表明相对于现有模型表现显著更优，充分说明了该模型的泛化性和有效性。