{"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/a-novel-geo-localization-method-for-uav-and","title":"A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention","arxiv_id":null,"date":"2023-09-23","proceeding":"Remote Sensing 2023 9","authors":["Z Cui","P Zhou","X Wang","Z Zhang","Y Li","H Li","Y Zhang"],"abstract":"Geo-localization has been widely applied as an important technique to get the longitude\r\nand latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible\r\ninterference and blocking of GPS signals, the method based on image retrieval, which is less likely\r\nto be interfered with, has received extensive attention in recent years. The geo-localization of UAVs\r\nand satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone\r\nimages from different perspectives. In this paper, an image transformation technique is used to extract\r\ncross-view geo-localization information from UAVs and satellites. A single-stage training method\r\nin UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view\r\nfeature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training\r\ntechniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters\r\nbeing trapped in suboptimal sets caused by the lack of constraint on positive and negative samples.\r\nThe results illustrate that the proposed loss function enhances image retrieval accuracy and realizes\r\na better convergence. Moreover, a data augmentation method for satellite images is proposed to\r\novercome the disproportionate numbers of image samples. On the benchmark University-1652, the\r\nproposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1)\r\nand 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.","url_abs":"https://www.mdpi.com/2072-4292/15/19/4667","url_pdf":"https://www.mdpi.com/2072-4292/15/19/4667/pdf?version=1695461087","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":"a-novel-geo-localization-method-for-uav-and","repo_url":"https://github.com/zfcui33/CCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"blocking","task_name":"Blocking"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"drone-view-target-localization","task_name":"Drone-view target localization"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":null,"task_name":"Triplet"},{"task_slug":"geo-localization","task_name":"geo-localization"}],"methods":[{"method_slug":"gps","method_name":"GPS"},{"method_slug":"triplet-loss","method_name":"Triplet Loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drone-view-target-localization-on-university-1","task":"Drone-view target localization","dataset":"University-1652","model":"Cross-view Consistent Attention","rank_in_archive_order":4,"of":11,"metrics":{"AP":"93.31","Recall@1":"91.57"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}