Deep learning (DL)-based methods have recently shown great promise in bitemporal change detection (CD). Existing discriminative methods based on Convolutional Neural Networks (CNNs) and Transformers rely on discriminative representation learning for change recognition while struggling with exploring local and long-range contextual dependencies. As a result, it is still challenging to obtain fine-grained and robust CD maps in diverse ground scenes. To cope with this challenge, this work proposes a generative change detection model called GCD-DDPM to directly generate CD maps by exploiting the Denoising Diffusion Probabilistic Model (DDPM), instead of classifying each pixel into changed or unchanged categories. Furthermore, the Difference Conditional Encoder (DCE), is designed to guide the generation of CD maps by exploiting multi-level difference features. Leveraging the variational inference (VI) procedure, GCD-DDPM can adaptively re-calibrate the CD results through an iterative inference process, while accurately distinguishing subtle and irregular changes in diverse scenes. Finally, a Noise Suppression-based Semantic Enhancer (NSSE) is specifically designed to mitigate noise in the current step's change-aware feature representations from the CD Encoder. This refinement, serving as an attention map, can guide subsequent iterations while enhancing CD accuracy. Extensive experiments on four high-resolution CD datasets confirm the superior performance of the proposed GCD-DDPM. The code for this work will be available at https://github.com/udrs/GCD.

翻译：摘要：基于深度学习的方法近期在双时相变化检测领域展现出巨大潜力。现有基于卷积神经网络（CNN）和Transformer的判别式方法依赖于判别式表征学习进行变化识别，但在探索局部与长程上下文依赖关系方面存在困难。因此，在多类地面场景中获取精细且稳健的变化检测图仍具有挑战性。为应对这一挑战，本文提出一种名为GCD-DDPM的生成式变化检测模型，该模型利用去噪扩散概率模型直接生成变化检测图，而非对每个像素进行变化/非变化分类。此外，本文设计了差异条件编码器，通过利用多层级差异特征引导变化检测图的生成。借助变分推理流程，GCD-DDPM可通过迭代推理过程自适应重校准检测结果，同时准确区分多样场景中的细微与不规则变化。最终，专为抑制当前步骤变化编码器输出的变化感知特征表示中的噪声，本文设计了基于噪声抑制的语义增强器。该增强器作为注意力图，可在提升变化检测精度的同时引导后续迭代过程。在四个高分辨率变化检测数据集上的大量实验证实了所提GCD-DDPM的优越性能。本工作代码将开源至https://github.com/udrs/GCD。