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

• URL: http://arxiv.org/abs/2206.11892v3
• Date: Fri, 12 Jan 2024 14:37:36 GMT
• Title: DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection
• Authors: Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, Vishal M. Patel
• Abstract summary: We introduce a novel approach for change detection by pre-training a Deno Diffusionising Probabilistic Model (DDPM) DDPM learns 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. Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing change detection methods in terms of F1 score, I
• Score: 31.125812018296127
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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