{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/remote-sensing-change-detection-segmentation","title":"DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection","arxiv_id":"2206.11892","date":"2022-06-23","proceeding":null,"authors":["Wele Gedara Chaminda Bandara","Nithin Gopalakrishnan Nair","Vishal M. Patel"],"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","url_abs":"https://arxiv.org/abs/2206.11892v3","url_pdf":"https://arxiv.org/pdf/2206.11892v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"remote-sensing-change-detection-segmentation","repo_url":"https://github.com/wgcban/ddpm-cd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/change-detection-on-cdd-dataset-season-1","task":"Change Detection","dataset":"CDD Dataset (season-varying)","model":"DDPM-CD","rank_in_archive_order":12,"of":18,"metrics":{"F1-Score":"95.62","Overall Accuracy":"98.98"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-dsifn-cd","task":"Change Detection","dataset":"DSIFN-CD","model":"DDPM-CD","rank_in_archive_order":1,"of":9,"metrics":{"F1":"96.65","Overall Accuracy":"97.09"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-levir-cd","task":"Change Detection","dataset":"LEVIR-CD","model":"DDPM-CD","rank_in_archive_order":20,"of":28,"metrics":{"F1":"90.91","Overall Accuracy":"99.09"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-whu-cd","task":"Change Detection","dataset":"WHU-CD","model":"DDPM-CD","rank_in_archive_order":8,"of":22,"metrics":{"F1":"92.65","Overall Accuracy":"99.42"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.11892","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.11892"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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