DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Remote Sensing Change Detection

Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, Vishal Patel; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5250-5262

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 self supervised 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. Code and pre-trained models available at https://github.com/wgcban/ddpm-cd