Remote sensing change detection is crucial for understanding the dynamics of our planet's surface, facilitating the monitoring of environmental changes, evaluating human impact, predicting future trends, and supporting decision-making. In this work, we introduce a novel approach for change detection that can leverage off-the-shelf, unlabeled remote sensing images in the training process by pre-training a Denoising Diffusion Probabilistic Model (DDPM) - a class of generative models used in image synthesis. DDPMs learn the training data distribution by gradually converting training images into a Gaussian distribution using a Markov chain. During inference (i.e., sampling), they can generate a diverse set of samples closer to the training distribution, starting from Gaussian noise, achieving state-of-the-art image synthesis results. However, in this work, our focus is not on image synthesis but on utilizing it as a pre-trained feature extractor for the downstream application of change detection. Specifically, we fine-tune a lightweight change classifier utilizing the feature representations produced by the pre-trained DDPM alongside change labels. Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing state-of-the-art change detection methods in terms of F1 score, IoU, and overall accuracy, highlighting the pivotal role of pre-trained DDPM as a feature extractor for downstream applications. We have made both the code and pre-trained models available at https://github.com/wgcban/ddpm-cd

翻译：遥感变化检测对于理解地球表面动态、监测环境变化、评估人类活动影响、预测未来趋势及支持决策制定至关重要。本文提出一种新颖的变化检测方法，通过预训练去噪扩散概率模型（DDPM，一类用于图像合成的生成模型），在训练过程中利用现成的无标签遥感图像。DDPM通过马尔可夫链将训练图像逐步转换为高斯分布，从而学习训练数据分布。在推理（即采样）阶段，该模型可从高斯噪声出发生成更贴近训练分布的多样化样本，实现了最先进的图像合成性能。然而，本文的研究重点并非图像合成，而是将其作为预训练特征提取器，服务于下游变化检测任务。具体而言，我们利用预训练DDPM生成的特征表示，结合变化标签对轻量化变化分类器进行微调。在LEVIR-CD、WHU-CD、DSIFN-CD和CDD数据集上的实验表明，所提出的DDPM-CD方法在F1分数、IoU和总体精度上显著优于现有最优变化检测方法，凸显了预训练DDPM作为特征提取器在下游应用中的关键作用。我们已将代码和预训练模型开放于https://github.com/wgcban/ddpm-cd。