{"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/maskchanger-a-transformer-based-model","title":"MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification","arxiv_id":null,"date":"2024-03-06","proceeding":"13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP) 2024 3","authors":["Mohammad Ebrahimzadeh","Mohammad Taghi Manzuri"],"abstract":"Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated change interpretation challenging. We propose MaskChanger - a novel deep learning paradigm tailored for satellite image change detection. Our method adapts the segmentation-specialized Mask2Former architecture by incorporating Siamese networks to extract features separately from bi-temporal images, while retaining the original mask transformer decoder. To our knowledge, this is the first study in which change detection is converted from the existing per-pixel classification approach into a mask classification approach. Evaluated on the LEVIR-CD benchmark of over 600 very high-resolution image pairs exhibiting real-world rural and urban changes, MaskChanger achieves Fl-Score of 91.96%, outperforming prior transformer-based change detection approaches.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10491166","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10491166","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":[],"tasks":[{"task_slug":"building-change-detection-for-remote-sensing","task_name":"Building change detection for remote sensing images"},{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/building-change-detection-for-remote-sensing","task":"Building change detection for remote sensing images","dataset":"LEVIR-CD","model":"MaskChanger (Swin-T)","rank_in_archive_order":8,"of":37,"metrics":{"F1":"91.96","IoU":"85.12"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}