{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:27:54Z","timestamp":1761719274077,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T00:00:00Z","timestamp":1706227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFA1004100","42301535","61673017","61403398"],"award-info":[{"award-number":["2022YFA1004100","42301535","61673017","61403398"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFA1004100","42301535","61673017","61403398"],"award-info":[{"award-number":["2022YFA1004100","42301535","61673017","61403398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating the geographic positions in GPS-denied environments is of great significance to the safe flight of unmanned aerial vehicles (UAVs). In this paper, we propose a novel geographic position estimation method for UAVs after road network alignment. We discuss the generally overlooked issue, namely, how to estimate the geographic position of the UAV after successful road network alignment, and propose a precise robust solution. In our method, the optimal initial solution of the geographic position of the UAV is first estimated from the road network alignment result, which is typically presented as a homography transformation between the observed road map and the reference one. The geographic position estimation is then modeled as an optimization problem to align the observed road with the reference one to improve the estimation accuracy further. Experiments on synthetic and real flight aerial image datasets show that the proposed algorithm can estimate more accurate geographic position of the UAV in real time and is robust to the errors from homography transformation estimation compared to the currently commonly-used method.<\/jats:p>","DOI":"10.3390\/rs16030482","type":"journal-article","created":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T10:54:27Z","timestamp":1706266467000},"page":"482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Geolocalization from Aerial Sensing Images Using Road Network Alignment"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0282-3073","authenticated-orcid":false,"given":"Yongfei","family":"Li","sequence":"first","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4948-4056","authenticated-orcid":false,"given":"Dongfang","family":"Yang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Shi","sequence":"additional","affiliation":[{"name":"Rearch Institute for Mathematics and Mathematical Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1294-8283","authenticated-orcid":false,"given":"Deyu","family":"Meng","sequence":"additional","affiliation":[{"name":"Rearch Institute for Mathematics and Mathematical Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2703","DOI":"10.1007\/s12145-022-00791-x","article-title":"A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment","volume":"15","author":"Patoliya","year":"2022","journal-title":"Earth Sci. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"387308","DOI":"10.1155\/2009\/387308","article-title":"Vision-based unmanned aerial vehicle navigation using geo-referenced information","volume":"2009","author":"Conte","year":"2009","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_3","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 2015 IEEE International Conference on Robotics and Biomimetics, Zhuhai, China.","DOI":"10.1109\/ROBIO.2015.7418753"},{"key":"ref_4","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 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943040"},{"key":"ref_5","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 2019 IEEE International Conference on Robotics and Automation, Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793558"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nassar, A. (2018, January 18\u201322). A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00201"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, L., and Hu, Y. (2009, January 20\u201322). Vision-aided navigation for aircrafts based on road junction detection. Proceedings of the 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, Shanghai, China.","DOI":"10.1109\/ICICISYS.2009.5357697"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10846-014-0056-3","article-title":"Airborne vision-aided navigation using road intersection features","volume":"78","author":"Dumble","year":"2015","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chang, C.H., Chou, C.N., and Chang, E.Y. (2017, January 21\u201326). CLKN: Cascaded Lucas-Kanade networks for image alignment. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Hawaii Convention Center, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.402"},{"key":"ref_10","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10232","DOI":"10.1109\/LRA.2022.3191038","article-title":"Season-Invariant GNSS-Denied Visual Localization for UAVs","volume":"7","author":"Kinnari","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hao, Y., He, M., Liu, Y., Liu, J., and Meng, Z. (2023). Range\u2013Visual\u2013Inertial Odometry with Coarse-to-Fine Image Registration Fusion for UAV Localization. Drones, 7.","DOI":"10.3390\/drones7080540"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). SuperPoint: Self-Supervised Interest Point Detection and Description. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_14","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3188257","article-title":"Semantic Segmentation of Remote Sensing Images With Sparse Annotations","volume":"19","author":"Hua","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","article-title":"Visual pattern recognition by moment invariants","volume":"8","author":"Hu","year":"1962","journal-title":"IRE Trans. Inf. Theory"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.imavis.2016.05.014","article-title":"Aerial image sequence geolocalization with road traffic as invariant feature","volume":"52","author":"Fraundorfer","year":"2016","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6065","DOI":"10.1109\/TGRS.2020.3011034","article-title":"Road-network-based fast geolocalization","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, S., He, H., Meng, D., and Yang, D. (2021). Fast aerial image geolocalization using the projective-invariant contour feature. Remote Sens., 13.","DOI":"10.3390\/rs13030490"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TNNLS.2020.3015660","article-title":"Attention-based road registration for GPS-denied UAS Navigation","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","first-page":"742","article-title":"Motion and Structure from Motion in a Piecewise Planar Environment","volume":"2","author":"Henriques","year":"1998","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_23","unstructured":"Malis, E., and Vargas, M. (2007). Deeper Understanding of the Homography Decomposition for Vision-Based Control, INRIA. Research Report-6303."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s001380050120","article-title":"A compact algorithm for rectification of stereo pairs","volume":"12","author":"Fusiello","year":"2000","journal-title":"Mach. Vis. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TPAMI.2004.17","article-title":"An efficient solution to the five-point relative pose problem","volume":"26","author":"Nister","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1142\/S0218001411008774","article-title":"A Stable Direct Solution of Perspective-Three-Point Problem","volume":"25","author":"Li","year":"2011","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","article-title":"EPnP: An Accurate O(n) Solution to the PnP Problem","volume":"81","author":"Lepetit","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hartley, R.I., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_29","unstructured":"Felzenszwalb, P., and Huttenlocher, D. (2004). Distance Transforms of Sampled Functions, Cornell University. Technical Report."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1007\/s10514-020-09927-8","article-title":"Geolocalization with aerial image sequence for UAVs","volume":"44","author":"Li","year":"2020","journal-title":"Auton. Robot."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/482\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:49:51Z","timestamp":1760104191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/482"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,26]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030482"],"URL":"https:\/\/doi.org\/10.3390\/rs16030482","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,1,26]]}}}