Deep learning (DL)-based methods have recently shown great promise in bitemporal change detection (CD). However, most existing methods are ineffective in simultaneously capturing long-range dependencies and exploiting local spatial information, resulting in inaccurate CD maps with discerning edges. To overcome these obstacles, a novel Denoising Diffusion Probabilistic Model (DDPM)-based generative CD approach called GCD-DDPM is proposed for remote sensing data. More specifically, GCD-DDPM is designed to directly generate CD maps by leveraging variational inference, which enables GCD-DDPM to accurately distinguish subtle and irregular buildings or natural scenes from the background. Furthermore, an adaptive calibration conditional difference encoding technique is proposed for GCD-DDPM to enhance the CD map through guided sampling of the differences among multi-level features. Finally, a noise suppression-based semantic enhancer (NSSE) is devised to cope with the high-frequency noise incurred in the CD map by capitalizing on the prior knowledge derived from the current step. Extensive experiments on four CD datasets, namely CDD, WHU, Levier and GVLM, confirm the good performance of the proposed GCD-DDPM.

翻译：基于深度学习的方法近年来在双时相变化检测中展现出巨大潜力。然而，现有大多数方法在同时捕获长距离依赖关系与利用局部空间信息方面存在不足，导致生成的变化检测图边缘不清晰。为克服这些障碍，本文提出一种基于去噪扩散概率模型的新型生成式变化检测方法GCD-DDPM，适用于遥感数据。具体而言，GCD-DDPM通过利用变分推断直接生成变化检测图，能够精准区分背景中细微、不规则建筑物或自然场景。此外，针对GCD-DDPM设计了一种自适应校准条件差异编码技术，通过对多层级特征差异进行引导采样来增强变化检测图。最后，提出基于噪声抑制的语义增强器，利用当前步骤的先验知识处理变化检测图中的高频噪声。在CDD、WHU、Levier和GVLM四个变化检测数据集上的大量实验证实了GCD-DDPM的优异性能。