{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T05:15:13Z","timestamp":1780118113004,"version":"3.54.0"},"reference-count":28,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071340"],"award-info":[{"award-number":["42071340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["221100211000-01"],"award-info":[{"award-number":["221100211000-01"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program of Song Shan Laboratory","award":["42071340"],"award-info":[{"award-number":["42071340"]}]},{"name":"Program of Song Shan Laboratory","award":["221100211000-01"],"award-info":[{"award-number":["221100211000-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) are widely used in many industries. The use of UAV images for surveying requires that the images contain high-precision localization information. However, the accuracy of UAV localization can be compromised in complex GNSS environments. To address this challenge, this study proposed a scheme to improve the localization accuracy of UAV sequences. The combination of traditional and deep learning methods can achieve rapid improvement of UAV image localization accuracy. Initially, individual UAV images with high similarity were selected using an image retrieval and localization method based on cosine similarity. Further, based on the relationships among UAV sequence images, short strip sequence images were selected to facilitate approximate location retrieval. Subsequently, a deep learning image registration network, combining SuperPoint and SuperGlue, was employed for high-precision feature point extraction and matching. The RANSAC algorithm was applied to eliminate mismatched points. In this way, the localization accuracy of UAV images was improved. Experimental results demonstrate that the mean errors of this approach were all within 2 pixels. Specifically, when using a satellite reference image with a resolution of 0.30 m\/pixel, the mean error of the UAV ground localization method reduced to 0.356 m.<\/jats:p>","DOI":"10.3390\/s23249751","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T14:12:51Z","timestamp":1702303971000},"page":"9751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Enhancing the Localization Accuracy of UAV Images under GNSS Denial Conditions"],"prefix":"10.3390","volume":"23","author":[{"given":"Han","family":"Gao","sequence":"first","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"},{"name":"31016 Troops, Beijing 100088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"61175 Troops, Nanjing 210049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Song","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","unstructured":"Akhloufi, M.A., Castro, N.A., and Couturier, A. (2018, January 16\u201318). UAVs for wildland fires. Proceedings of the Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, Orlando, FL, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Akhloufi, M.A., Castro, N.A., and Couturier, A. (2021). Unmanned aerial systems for wildland and forest fires: Sensing, perception, cooperation and assistance. Drones, 5.","DOI":"10.3390\/drones5010015"},{"key":"ref_4","first-page":"112","article-title":"Studying the effect of difficult fire conditions on the quality of observation and safety of UAV flight","volume":"1","author":"Mokrova","year":"2021","journal-title":"Izv. YuFU. Tekhnicheskie Nauk."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1049\/iet-rsn.2017.0251","article-title":"State-of-theart technologies for UAV inspections","volume":"12","author":"Jordan","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Scherer, J., Yahyanejad, S., Hayat, S., Yanmaz, E., Andre, T., Khan, A., Vukadinovic, V., Bettstetter, C., Hellwagner, H., and Rinner, B. (2015, January 18). An autonomous multiUAV system for search and rescue. Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use, DroNet\u201915, ACM, New York, NY, USA,.","DOI":"10.1145\/2750675.2750683"},{"key":"ref_7","unstructured":"Mittal, M., Mohan, R., Burgard, W., and Valada, A. (2019, January 6\u201310). Vision-based autonomous UAV navigation and landing for urban search and rescue. Proceedings of the International Symposium on Robotics Research (ISRR), Hanoi, Vietnam."},{"key":"ref_8","first-page":"34","article-title":"Intelligent system of computer vision of unmanned aerial vehicles for monitoring technological facilities of oil and gas companies","volume":"330","author":"Zoev","year":"2019","journal-title":"Izv. Tomsk. Politekh. Universiteta. Inzhiniring Georesursov"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"82930","DOI":"10.1109\/ACCESS.2021.3087084","article-title":"UAVouch: A secure identity and location validation scheme for UAV-networks","volume":"9","author":"Silva","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1134\/S2075108722010059","article-title":"High-precision navigation independently of global navigation satellite systems data","volume":"13","author":"Peshekhonov","year":"2022","journal-title":"Gyroscopy Navig."},{"key":"ref_11","unstructured":"Sabatini, R., Moore, T., Hill, C., and Ramasamy, S. (2015, January 23\u201324). Avionics-based GNSS integrity augmentation performance in a jamming environment. Proceedings of the AIAC16: 16th Australian International Aerospace Congress, Engineers Australia, Melbourne, Australia."},{"key":"ref_12","unstructured":"Groves, P.D., Jiang, Z., Rudi, M., and Strode, P. (2013, January 16\u201320). A portfolio approach to NLOS and multipath mitigation in dense urban areas. Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation, The Institute of Navigation, Nashville, TN, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Conte, G., and Doherty, P. (2008, January 1\u20138). An integrated UAV navigation system based on aerial image matching. Proceedings of the 2008 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2008.4526556"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Viswanathan, A., Pires, B.R., and Huber, D. (2016, January 16\u201321). Vision- based robot localization across seasons and in remote locations. Proceedings of the International Conference on Robotics and Automation, Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487685"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1134\/S207510871902007X","article-title":"GPS based navigation systems in difficult environments","volume":"10","author":"Schmidt","year":"2019","journal-title":"Gyroscopy Navig."},{"key":"ref_16","first-page":"575","article-title":"Translating aerial images into street-map representations for visual self-localization of UAVs, ISPRS-International Archives of the Photogrammetry","volume":"42","author":"Schleiss","year":"2019","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yol, A., Delabarre, B., Dame, A., Dartois, J.-E., and Marchand, E. (2014, January 14\u201318). Vision- based absolute localization for unmanned aerial vehicles. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943040"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/TIP.2012.2199124","article-title":"Second-order optimization of mutual information for real-time image registration","volume":"21","author":"Dame","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shan, M., Wang, F., Lin, F., Gao, Z., Tang, Y.Z., and Chen, B.M. (2015, January 6\u20139). Google map aided visual navigation for UAVs in GPS-denied environment. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China.","DOI":"10.1109\/ROBIO.2015.7418753"},{"key":"ref_21","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Soc. Conference Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_22","first-page":"89","article-title":"Uav Visual Autolocalizaton Based on Automatic Landmark Recognition","volume":"IV\u20132\/W3","author":"Shiguemori","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Goforth, H., and Lucey, S. (2019, January 20\u201324). GPS-denied UAV localization using pre-existing satellite imagery. Proceedings of the International Conference on Robotics and Automation (ICRA), IEEE, Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793558"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition, Conference ICLR. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saranya, K.C., Naidu, V.P.S., Singhal, V., and Tanuja, B.M. (2016, January 6\u20137). Application of vision-based techniques for UAV position estimation. Proceedings of the International Conference on Research Advances in Integrated Navigation Systems (RAINS), Bangalore, India.","DOI":"10.1109\/RAINS.2016.7764392"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, X., Kealy, A., Li, W., Jelfs, B., Gilliam, C., May, S.L., and Moran, B. (2021). Toward autonomous UAV localization via aerial image registration. Electronics, 10.","DOI":"10.3390\/electronics10040435"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018). SuperPoint: Self-Supervised Interest Point Detection and Description. arXiv.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, H., Wang, P., and Jing, L. (2020). An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment. Sensors, 20.","DOI":"10.3390\/s20082286"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9751\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:36:32Z","timestamp":1760132192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":28,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249751"],"URL":"https:\/\/doi.org\/10.3390\/s23249751","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,11]]}}}