{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:24:36Z","timestamp":1772601876086,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Science and Technology Major Project","award":["AA22068072"],"award-info":[{"award-number":["AA22068072"]}]},{"name":"Guangxi Science and Technology Major Project","award":["ASFC-2019460S5001"],"award-info":[{"award-number":["ASFC-2019460S5001"]}]},{"name":"Guangxi Science and Technology Major Project","award":["220100013"],"award-info":[{"award-number":["220100013"]}]},{"name":"Guangxi Science and Technology Major Project","award":["2021BCA216"],"award-info":[{"award-number":["2021BCA216"]}]},{"name":"Guangxi Science and Technology Major Project","award":["2022BCA057"],"award-info":[{"award-number":["2022BCA057"]}]},{"name":"Aeronautical Science Foundation of China","award":["AA22068072"],"award-info":[{"award-number":["AA22068072"]}]},{"name":"Aeronautical Science Foundation of China","award":["ASFC-2019460S5001"],"award-info":[{"award-number":["ASFC-2019460S5001"]}]},{"name":"Aeronautical Science Foundation of China","award":["220100013"],"award-info":[{"award-number":["220100013"]}]},{"name":"Aeronautical Science Foundation of China","award":["2021BCA216"],"award-info":[{"award-number":["2021BCA216"]}]},{"name":"Aeronautical Science Foundation of China","award":["2022BCA057"],"award-info":[{"award-number":["2022BCA057"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["AA22068072"],"award-info":[{"award-number":["AA22068072"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["ASFC-2019460S5001"],"award-info":[{"award-number":["ASFC-2019460S5001"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["220100013"],"award-info":[{"award-number":["220100013"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["2021BCA216"],"award-info":[{"award-number":["2021BCA216"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["2022BCA057"],"award-info":[{"award-number":["2022BCA057"]}]},{"name":"The Key Research and Development Project of Hubei Province","award":["AA22068072"],"award-info":[{"award-number":["AA22068072"]}]},{"name":"The Key Research and Development Project of Hubei Province","award":["ASFC-2019460S5001"],"award-info":[{"award-number":["ASFC-2019460S5001"]}]},{"name":"The Key Research and Development Project of Hubei Province","award":["220100013"],"award-info":[{"award-number":["220100013"]}]},{"name":"The Key Research and Development Project of Hubei Province","award":["2021BCA216"],"award-info":[{"award-number":["2021BCA216"]}]},{"name":"The Key Research and Development Project of Hubei Province","award":["2022BCA057"],"award-info":[{"award-number":["2022BCA057"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Visual geo-localization can achieve UAVs (Unmanned Aerial Vehicles) position during GNSS (Global Navigation Satellite System) denial or restriction. However, The performance of visual geo-localization is seriously impaired by illumination variation, different scales, viewpoint difference, spare texture, and computer power of UAVs, etc. In this paper, a fast detector-free two-stage matching method is proposed to improve the visual geo-localization of low-altitude UAVs. A detector-free matching method and perspective transformation module are incorporated into the coarse and fine matching stages to improve the robustness of the weak texture and viewpoint data. The minimum Euclidean distance is used to accelerate the coarse matching, and the coordinate regression based on DSNT (Differentiable Spatial to Numerical) transform is used to improve the fine matching accuracy respectively. The experimental results show that the average localization precision of the proposed method is 2.24 m, which is 0.33 m higher than that of the current typical matching methods. In addition, this method has obvious advantages in localization robustness and inference efficiency on Jetson Xavier NX, which completed to match and localize all images in the dataset while the localization frequency reached the best.<\/jats:p>","DOI":"10.3390\/rs14225879","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:33:32Z","timestamp":1669005212000},"page":"5879","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Fast and Robust Heterologous Image Matching Method for Visual Geo-Localization of Low-Altitude UAVs"],"prefix":"10.3390","volume":"14","author":[{"given":"Haigang","family":"Sui","sequence":"first","affiliation":[{"name":"State Key Laboratory Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7249-4036","authenticated-orcid":false,"given":"Jiajie","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China"}]},{"given":"Junfeng","family":"Lei","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0580-7017","authenticated-orcid":false,"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7198-4735","authenticated-orcid":false,"given":"Guohua","family":"Gou","sequence":"additional","affiliation":[{"name":"State Key Laboratory Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117734","DOI":"10.1016\/j.eswa.2022.117734","article-title":"A Survey of State-of-the-Art on Visual SLAM","volume":"205","author":"Kazerouni","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104069","DOI":"10.1016\/j.robot.2022.104069","article-title":"A review of GNSS-independent UAV navigation techniques","volume":"152","author":"Gyagenda","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103666","DOI":"10.1016\/j.robot.2020.103666","article-title":"A review on absolute visual localization for UAV","volume":"135","author":"Couturier","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"76847","DOI":"10.1109\/ACCESS.2021.3082778","article-title":"State of the art in vision-based localization techniques for autonomous navigation systems","volume":"9","author":"Alkendi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","first-page":"269","article-title":"A survey of visual odometry","volume":"13","author":"Hu","year":"2021","journal-title":"Nanjing Xinxi Gongcheng Daxue Xuebao"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1017\/S0373463316000187","article-title":"Multi-region scene matching based localisation for autonomous vision navigation of UAVs","volume":"69","author":"Jin","year":"2016","journal-title":"J. Navig."},{"key":"ref_7","first-page":"1","article-title":"Full-parameter vision navigation based on scene matching for aircrafts","volume":"57","author":"Yu","year":"2014","journal-title":"Sci. China Inf. Sci."},{"key":"ref_8","first-page":"785","article-title":"Image Matching Techniques: A Review","volume":"401","author":"Kaur","year":"2021","journal-title":"Inf. Commun. Technol. Compet. Strateg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2022.06.003","article-title":"Deep learning feature representation for image matching under large viewpoint and viewing direction change","volume":"190","author":"Chen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.012","article-title":"A review of multimodal image matching: Methods and applications","volume":"73","author":"Jiang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11263-020-01359-2","article-title":"Image matching from handcrafted to deep features: A survey","volume":"129","author":"Ma","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yao, G., Yilmaz, A., Meng, F., and Zhang, L. (2021). Review of Wide-Baseline Stereo Image Matching Based on Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13163247"},{"key":"ref_13","first-page":"10","article-title":"A combined corner and edge detector","volume":"Volume 15","author":"Harris","year":"1988","journal-title":"Alvey Vision Conference"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/S0262-8856(97)00056-5","article-title":"Fast corner detection","volume":"16","author":"Hedley","year":"1998","journal-title":"Image Vis. Comput."},{"key":"ref_15","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 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: Binary robust independent elementary features. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15561-1_56"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"LOWE","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","first-page":"404","article-title":"Surf: Speeded up robust features","volume":"Volume 3951","author":"Bay","year":"2006","journal-title":"European Conference on Computer Vision"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_20","first-page":"467","article-title":"Lift: Learned invariant feature transform","volume":"Volume 9910","author":"Yi","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref_21","unstructured":"Zhang, X., Yu, F., Karaman, S., and Chang, S. Learning discriminative and transformation covariant local feature detectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_22","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_23","unstructured":"Ono, Y., Trulls, E., Fua, P., and Yi, K.M. (2018). LF-Net: Learning local features from images. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_24","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. D2-net: A trainable cnn for joint description and detection of local features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_25","unstructured":"Chen, H., Luo, Z., Zhang, J., Zhou, L., Bai, X., Hu, Z., Tai, C., and Quan, L. Learning to match features with seeded graph matching network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_26","unstructured":"Efe, U., Ince, K.G., and Alatan, A. Dfm: A performance baseline for deep feature matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"ref_27","unstructured":"Revaud, J., Leroy, V., Weinzaepfel, P., and Chidlovskii, B. PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","article-title":"Sift flow: Dense correspondence across scenes and its applications","volume":"33","author":"Liu","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","unstructured":"Choy, C.B., Gwak, J., Savarese, S., and Chandraker, M. (2016). Universal correspondence network. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/LRA.2016.2634089","article-title":"Self-supervised visual descriptor learning for dense correspon-dence","volume":"2","author":"Schmidt","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1109\/TPAMI.2020.3016711","article-title":"Ncnet: Neighbourhood consensus networks for estimating image correspondences","volume":"44","author":"Rocco","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, J., and Zhang, X. (2022, January 23\u201327). DRC-NET: Densely Connected Recurrent Convolutional Neural Network for Speech Dereverberation. Proceedings of the ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747111"},{"key":"ref_33","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_34","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., and Rabinovich, A. Superglue: Learning feature matching with graph neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_35","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X. LoFTR: Detector-free local feature matching with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, J., Yang, K., Peng, K., and Stiefelhagen, R. (2022). MatchFormer: Interleaving Attention in Transformers for Feature Matching, Karlsruhe Institute of Technology. to be submitted.","DOI":"10.1007\/978-3-031-26313-2_16"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Y., Tao, J., Kong, D., Zhang, Y., and Li, P. (2022). A Visual Compass Based on Point and Line Features for UA V High-Altitude Orientation Estimation. Remote Sens., 14.","DOI":"10.3390\/rs14061430"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ma, G., and Wu, J. (2022). Air-Ground Multi-Source Image Matching Based on High-Precision Reference Image. Remote Sens., 14.","DOI":"10.3390\/rs14030588"},{"key":"ref_39","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wen, K., Chu, J., Chen, J., Chen, Y., and Cai, J. (2022). MO SiamRPN with Weight Adaptive Joint MIoU for UAV Visual Localization. Remote Sens., 14.","DOI":"10.3390\/rs14184467"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/TCSVT.2021.3061265","article-title":"Each part matters: Local patterns facilitate cross-view geo-localization","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_42","unstructured":"Zheng, Z., Wei, Y., and Yang, Y. University-1652: A multi-view multi-source benchmark for drone-based geo-localization. Proceedings of the 28th ACM International Conference on Multimedia."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ding, L., Zhou, J., Meng, L., and Long, Z. (2020). A practical cross-view image matching method between UAV and satellite for UAV-based geo-localization. Remote Sens., 13.","DOI":"10.3390\/rs13010047"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Dai, M., Chen, X., and Zheng, E. (2021). A Faster and More Effective Cross-View Matching Method of UAV and Satellite Images for UAV Geolocalization. Remote Sens., 13.","DOI":"10.3390\/rs13193979"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, G., Zhao, Y., Tang, C., Luo, C., and Zeng, W. (2022). When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism, University of Science and Technology of China. to be submitted.","DOI":"10.1609\/aaai.v36i2.20142"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lee-Thorp, J., Ainslie, J., Eckstein, I., and Ontanon, S. (2022, January 10\u201315). FNet: Mixing tokens with fourier transforms. Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), Seattle, WA, USA.","DOI":"10.18653\/v1\/2022.naacl-main.319"},{"key":"ref_47","unstructured":"Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., and Yan, S. Metaformer is actually what you need for vision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"ref_48","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_50","unstructured":"Nibali, A., He, Z., Morgan, S., and Prendergast, L. (2018). Numerical Coordinate Regression with Convolutional Neural Networks, La Trobe University. to be submitted."},{"key":"ref_51","unstructured":"Li, Z., and Snavely, N. Megadepth: Learning single-view depth prediction from internet photos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_53","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K. HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"ref_54","unstructured":"Zhou, Q., Sattler, T., and Leal-Taixe, L. Patch2pix: Epipolar-guided pixel-level correspondences. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5879\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:14Z","timestamp":1760145734000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5879"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,20]]},"references-count":54,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225879"],"URL":"https:\/\/doi.org\/10.3390\/rs14225879","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,20]]}}}