MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization
Cross-view geo-localization (CVGL) presents significant challenges due to the drastic variations in perspective and scene layout between unmanned aerial vehicle (UAV) and satellite images. Existing methods have made certain advancements in extracting local features from images. However, they exhibit...
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4519 |
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| author | Kaiji Hou Qiang Tong Na Yan Xiulei Liu Shoulu Hou |
| author_facet | Kaiji Hou Qiang Tong Na Yan Xiulei Liu Shoulu Hou |
| author_sort | Kaiji Hou |
| collection | DOAJ |
| description | Cross-view geo-localization (CVGL) presents significant challenges due to the drastic variations in perspective and scene layout between unmanned aerial vehicle (UAV) and satellite images. Existing methods have made certain advancements in extracting local features from images. However, they exhibit limitations in modeling the interactions among local features and fall short in aligning cross-view representations accurately. To address these issues, we propose a Multi-Scale Cascade and Feature Adaptive Alignment (MCFA) network, which consists of a Multi-Scale Cascade Module (MSCM) and a Feature Adaptive Alignment Module (FAAM). The MSCM captures the features of the target’s adjacent regions and enhances the model’s robustness by learning key region information through association and fusion. The FAAM, with its dynamically weighted feature alignment module, adaptively adjusts feature differences across different viewpoints, achieving feature alignment between drone and satellite images. Our method achieves state-of-the-art (SOTA) performance on two public datasets, University-1652 and SUES-200. In generalization experiments, our model outperforms existing SOTA methods, with an average improvement of 1.52% in R@1 and 2.09% in AP, demonstrating its effectiveness and strong generalization in cross-view geo-localization tasks. |
| format | Article |
| id | doaj-art-8a75425fa695442cadeefd6c30f34263 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8a75425fa695442cadeefd6c30f342632025-08-20T03:32:15ZengMDPI AGSensors1424-82202025-07-012514451910.3390/s25144519MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-LocalizationKaiji Hou0Qiang Tong1Na Yan2Xiulei Liu3Shoulu Hou4College of Computer Science, Beijing Information Science and Technology University, Beijing 102206, ChinaCollege of Computer Science, Beijing Information Science and Technology University, Beijing 102206, ChinaCollege of Computer Science, Beijing Information Science and Technology University, Beijing 102206, ChinaCollege of Computer Science, Beijing Information Science and Technology University, Beijing 102206, ChinaCollege of Computer Science, Beijing Information Science and Technology University, Beijing 102206, ChinaCross-view geo-localization (CVGL) presents significant challenges due to the drastic variations in perspective and scene layout between unmanned aerial vehicle (UAV) and satellite images. Existing methods have made certain advancements in extracting local features from images. However, they exhibit limitations in modeling the interactions among local features and fall short in aligning cross-view representations accurately. To address these issues, we propose a Multi-Scale Cascade and Feature Adaptive Alignment (MCFA) network, which consists of a Multi-Scale Cascade Module (MSCM) and a Feature Adaptive Alignment Module (FAAM). The MSCM captures the features of the target’s adjacent regions and enhances the model’s robustness by learning key region information through association and fusion. The FAAM, with its dynamically weighted feature alignment module, adaptively adjusts feature differences across different viewpoints, achieving feature alignment between drone and satellite images. Our method achieves state-of-the-art (SOTA) performance on two public datasets, University-1652 and SUES-200. In generalization experiments, our model outperforms existing SOTA methods, with an average improvement of 1.52% in R@1 and 2.09% in AP, demonstrating its effectiveness and strong generalization in cross-view geo-localization tasks.https://www.mdpi.com/1424-8220/25/14/4519cross-view geo-localizationunmanned aerial vehicles (UAVs)image retrievalremote sensing |
| spellingShingle | Kaiji Hou Qiang Tong Na Yan Xiulei Liu Shoulu Hou MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization Sensors cross-view geo-localization unmanned aerial vehicles (UAVs) image retrieval remote sensing |
| title | MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization |
| title_full | MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization |
| title_fullStr | MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization |
| title_full_unstemmed | MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization |
| title_short | MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization |
| title_sort | mcfa multi scale cascade and feature adaptive alignment network for cross view geo localization |
| topic | cross-view geo-localization unmanned aerial vehicles (UAVs) image retrieval remote sensing |
| url | https://www.mdpi.com/1424-8220/25/14/4519 |
| work_keys_str_mv | AT kaijihou mcfamultiscalecascadeandfeatureadaptivealignmentnetworkforcrossviewgeolocalization AT qiangtong mcfamultiscalecascadeandfeatureadaptivealignmentnetworkforcrossviewgeolocalization AT nayan mcfamultiscalecascadeandfeatureadaptivealignmentnetworkforcrossviewgeolocalization AT xiuleiliu mcfamultiscalecascadeandfeatureadaptivealignmentnetworkforcrossviewgeolocalization AT shouluhou mcfamultiscalecascadeandfeatureadaptivealignmentnetworkforcrossviewgeolocalization |