UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework
Cross-view image matching between UAV and satellite platforms is critical for geographic localization but remains challenging due to domain gaps caused by disparities in imaging sensors, viewpoints, and illumination conditions. To address these challenges, this paper proposes an Adaptive Threshold-g...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/14/2448 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849418933217525760 |
|---|---|
| author | Yushi Liao Juan Su Decao Ma Chao Niu |
| author_facet | Yushi Liao Juan Su Decao Ma Chao Niu |
| author_sort | Yushi Liao |
| collection | DOAJ |
| description | Cross-view image matching between UAV and satellite platforms is critical for geographic localization but remains challenging due to domain gaps caused by disparities in imaging sensors, viewpoints, and illumination conditions. To address these challenges, this paper proposes an Adaptive Threshold-guided Ring Partitioning Framework (ATRPF) for UAV–satellite cross-view image matching. Unlike conventional ring-based methods with fixed partitioning rules, ATRPF innovatively incorporates heatmap-guided adaptive thresholds and learnable hyperparameters to dynamically adjust ring-wise feature extraction regions, significantly enhancing cross-domain representation learning through context-aware adaptability. The framework synergizes three core components: brightness-aligned preprocessing to reduce illumination-induced domain shifts, hybrid loss functions to improve feature discriminability across domains, and keypoint-aware re-ranking to refine retrieval results by compensating for neural networks’ localization uncertainty. Comprehensive evaluations on the University-1652 benchmark demonstrate the framework’s superiority; it achieves 82.50% Recall@1 and 84.28% AP for UAV→Satellite geo-localization, along with 90.87% Recall@1 and 80.25% AP for Satellite→UAV navigation. These results validate the framework’s capability to bridge UAV–satellite domain gaps while maintaining robust matching precision under heterogeneous imaging conditions, providing a viable solution for practical applications such as UAV navigation in GNSS-denied environments. |
| format | Article |
| id | doaj-art-d2eba5fd0013438299e5bcf9d1b2e580 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d2eba5fd0013438299e5bcf9d1b2e5802025-08-20T03:32:18ZengMDPI AGRemote Sensing2072-42922025-07-011714244810.3390/rs17142448UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning FrameworkYushi Liao0Juan Su1Decao Ma2Chao Niu3Rocket Force University of Engineering, Xi’an 710025, ChinaRocket Force University of Engineering, Xi’an 710025, ChinaRocket Force University of Engineering, Xi’an 710025, ChinaRocket Force University of Engineering, Xi’an 710025, ChinaCross-view image matching between UAV and satellite platforms is critical for geographic localization but remains challenging due to domain gaps caused by disparities in imaging sensors, viewpoints, and illumination conditions. To address these challenges, this paper proposes an Adaptive Threshold-guided Ring Partitioning Framework (ATRPF) for UAV–satellite cross-view image matching. Unlike conventional ring-based methods with fixed partitioning rules, ATRPF innovatively incorporates heatmap-guided adaptive thresholds and learnable hyperparameters to dynamically adjust ring-wise feature extraction regions, significantly enhancing cross-domain representation learning through context-aware adaptability. The framework synergizes three core components: brightness-aligned preprocessing to reduce illumination-induced domain shifts, hybrid loss functions to improve feature discriminability across domains, and keypoint-aware re-ranking to refine retrieval results by compensating for neural networks’ localization uncertainty. Comprehensive evaluations on the University-1652 benchmark demonstrate the framework’s superiority; it achieves 82.50% Recall@1 and 84.28% AP for UAV→Satellite geo-localization, along with 90.87% Recall@1 and 80.25% AP for Satellite→UAV navigation. These results validate the framework’s capability to bridge UAV–satellite domain gaps while maintaining robust matching precision under heterogeneous imaging conditions, providing a viable solution for practical applications such as UAV navigation in GNSS-denied environments.https://www.mdpi.com/2072-4292/17/14/2448cross-view geo-localizationdomain gap adaptationUAV–satellite image matchingadaptive ring partitioningkeypoint re-ranking |
| spellingShingle | Yushi Liao Juan Su Decao Ma Chao Niu UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework Remote Sensing cross-view geo-localization domain gap adaptation UAV–satellite image matching adaptive ring partitioning keypoint re-ranking |
| title | UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework |
| title_full | UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework |
| title_fullStr | UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework |
| title_full_unstemmed | UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework |
| title_short | UAV-Satellite Cross-View Image Matching Based on Adaptive Threshold-Guided Ring Partitioning Framework |
| title_sort | uav satellite cross view image matching based on adaptive threshold guided ring partitioning framework |
| topic | cross-view geo-localization domain gap adaptation UAV–satellite image matching adaptive ring partitioning keypoint re-ranking |
| url | https://www.mdpi.com/2072-4292/17/14/2448 |
| work_keys_str_mv | AT yushiliao uavsatellitecrossviewimagematchingbasedonadaptivethresholdguidedringpartitioningframework AT juansu uavsatellitecrossviewimagematchingbasedonadaptivethresholdguidedringpartitioningframework AT decaoma uavsatellitecrossviewimagematchingbasedonadaptivethresholdguidedringpartitioningframework AT chaoniu uavsatellitecrossviewimagematchingbasedonadaptivethresholdguidedringpartitioningframework |