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

TL;DR

This paper introduces DDPM-CD, a novel change detection method that leverages pre-trained Denoising Diffusion Probabilistic Models as feature extractors, significantly improving accuracy on multiple remote sensing datasets.

Contribution

It presents a new approach using pre-trained DDPMs for feature extraction in change detection, outperforming existing methods.

Findings

Outperforms state-of-the-art methods in F1 score, IoU, and accuracy

Effective use of unlabeled remote sensing images for pre-training

Demonstrates robustness across multiple datasets

Abstract

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