{"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/an-end-to-end-supervised-domain-adaptation","title":"An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection","arxiv_id":"2204.00154","date":"2022-04-01","proceeding":null,"authors":["Jia Liu","Wenjie Xuan","Yuhang Gan","Juhua Liu","Bo Du"],"abstract":"Existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations, but ignore the universal domain shift induced by time-varying land cover changes, including luminance fluctuations and season changes between pre-event and post-event images, thereby producing sub-optimal results. In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions. Specifically, our SDACD presents collaborative adaptations from both image and feature perspectives with supervised learning. Image adaptation exploits generative adversarial learning with cycle-consistency constraints to perform cross-domain style transformation, effectively narrowing the domain gap in a two-side generation fashion. As to feature adaptation, we extract domain-invariant features to align different feature distributions in the feature space, which could further reduce the domain gap of cross-domain images. To further improve the performance, we combine three types of bi-temporal images for the final change prediction, including the initial input bi-temporal images and two generated bi-temporal images from the pre-event and post-event domains. Extensive experiments and analyses on two benchmarks demonstrate the effectiveness and universality of our proposed framework. Notably, our framework pushes several representative baseline models up to new State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU building datasets, respectively. The source code and models are publicly available at https://github.com/Perfect-You/SDACD.","url_abs":"https://arxiv.org/abs/2204.00154v2","url_pdf":"https://arxiv.org/pdf/2204.00154v2.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":"an-end-to-end-supervised-domain-adaptation","repo_url":"https://github.com/perfect-you/sdacd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"change-detection-for-remote-sensing-images","task_name":"Change detection for remote sensing images"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[{"method_slug":"align","method_name":"ALIGN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/change-detection-for-remote-sensing-images-on","task":"Change detection for remote sensing images","dataset":"CDD Dataset (season-varying)","model":"SDACD","rank_in_archive_order":6,"of":25,"metrics":{"F1-Score":"0.9734"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-for-remote-sensing-images-on-1","task":"Change detection for remote sensing images","dataset":"WHU building","model":"SDACD","rank_in_archive_order":2,"of":2,"metrics":{"F1-Score":"0.9236"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}