{"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/hcgmnet-a-hierarchical-change-guiding-map","title":"HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection","arxiv_id":"2302.10420","date":"2023-02-21","proceeding":null,"authors":["Chengxi Han","Chen Wu","Bo Du"],"abstract":"Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution operations to extract multiscale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods.","url_abs":"https://arxiv.org/abs/2302.10420v2","url_pdf":"https://arxiv.org/pdf/2302.10420v2.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":"hcgmnet-a-hierarchical-change-guiding-map","repo_url":"https://github.com/ChengxiHAN/HCGMNet-CD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/change-detection-on-cdd-dataset-season-1","task":"Change Detection","dataset":"CDD Dataset (season-varying)","model":"HCGMNet","rank_in_archive_order":14,"of":18,"metrics":{"F1":"95.07","F1-Score":"95.07","IoU":"90.60","KC":"94.40","Overall Accuracy":"98.82","Precision":"93.84","Recall":"96.34"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-dsifn-cd","task":"Change Detection","dataset":"DSIFN-CD","model":"HCGMNet","rank_in_archive_order":9,"of":9,"metrics":{"F1":"55.00","IoU":"37.93","KC":"41.53","Overall Accuracy":"76.26","Precision":"40.57","Recall":"85.35"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-googlegz-cd","task":"Change Detection","dataset":"GoogleGZ-CD","model":"HCGMNet","rank_in_archive_order":3,"of":4,"metrics":{"F1":"85.71","IoU":"74.99","KC":"80.94","Overal Accuracy":"92.85","Precision":"84.25","Recall":"87.22"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-levir","task":"Change Detection","dataset":"LEVIR+","model":"HCGMNet","rank_in_archive_order":7,"of":9,"metrics":{"F1":"82.37","IoU":"70.03","KC":"81.63","OA":"98.57","Prcision":"82.81","Recall":"81.94"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-levir-cd","task":"Change Detection","dataset":"LEVIR-CD","model":"HCGMNet","rank_in_archive_order":17,"of":28,"metrics":{"F1":"91.77","F1-score":"91.77","IoU":"84.79","Overall Accuracy":"99.18","Precision":"92.96","Recall":"90.61"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-s2looking","task":"Change Detection","dataset":"S2Looking","model":"HCGMNet","rank_in_archive_order":9,"of":11,"metrics":{"F1":"63.87","F1-Score":"63.87","IoU":"46.91","KC":"63.48","OA":"99.22","Precision":"72.51","Recall":"57.06"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-sysu-cd","task":"Change Detection","dataset":"SYSU-CD","model":"HCGMNet","rank_in_archive_order":9,"of":12,"metrics":{"F1":"79.76","IoU":"66.33","KC":"74.11","OA":"91.12","Precision":"86.28","Recall":"74.15"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-whu-cd","task":"Change Detection","dataset":"WHU-CD","model":"HCGMNet","rank_in_archive_order":12,"of":22,"metrics":{"F1":"92.08","IoU":"85.33","KC":"91.80","Overall Accuracy":"99.45","Precision":"93.93","Recall":"90.31"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2302.10420","atlas_url":"https://app.syntology.ai/?focus=2302.10420","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}