  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
