{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:17:17Z","timestamp":1771885037234,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2020YFG0240"],"award-info":[{"award-number":["2020YFG0240"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2020YFG0055"],"award-info":[{"award-number":["2020YFG0055"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015286","name":"Hebei Provincial Key Research Projects","doi-asserted-by":"publisher","award":["20355901D"],"award-info":[{"award-number":["20355901D"]}],"id":[{"id":"10.13039\/501100015286","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015286","name":"Hebei Provincial Key Research Projects","doi-asserted-by":"publisher","award":["19255901D"],"award-info":[{"award-number":["19255901D"]}],"id":[{"id":"10.13039\/501100015286","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.<\/jats:p>","DOI":"10.3390\/rs13183605","type":"journal-article","created":{"date-parts":[[2021,9,12]],"date-time":"2021-09-12T21:48:01Z","timestamp":1631483281000},"page":"3605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9534-592X","authenticated-orcid":false,"given":"Xin","family":"Luo","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Guangling","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yuwei","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Xixu","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Wenbo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Weimin","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, W., Li, C., and Wang, F. (August, January 28). Research on UAV image registration based on SIFT algorithm acceleration. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900483"},{"key":"ref_2","first-page":"1","article-title":"A Compilation of UAV applications for precision agriculture","volume":"2","author":"Yang","year":"2020","journal-title":"Smart Agric."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A review on UAV-based applications for precision agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision (ICCV), Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107434","DOI":"10.1016\/j.sigpro.2019.107434","article-title":"Image fusion employing adaptive spectral-spatial gradient sparse regularization in UAV remote sensing","volume":"170","author":"Zhang","year":"2020","journal-title":"Signal. Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jeong, D.M., Kim, J.H., Lee, Y.W., and Kim, B.G. (2018, January 29\u201331). Robust weighted keypoint matching algorithm for image retrieval. Proceedings of the 2nd International Conference on Video and Image Processing (ICVIP 2018), Hong Kong, China.","DOI":"10.1145\/3301506.3301513"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"025002","DOI":"10.1117\/1.JRS.12.025002","article-title":"Unmanned aerial vehicle oblique image registration using an ASIFT-based matching method","volume":"12","author":"Wang","year":"2018","journal-title":"J. Appl. Remote. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"SURF: Speeded up robust features","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hossein-Nejad, Z., and Nasri, M. (2016, January 6\u20138). Image registration based on SIFT features and adaptive RANSAC transform. Proceedings of the 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2016.7754318"},{"key":"ref_10","first-page":"135","article-title":"ISPRS benchmark for MultiPlatform photogrammetry","volume":"II-3\/W4","author":"Nex","year":"2015","journal-title":"ISPRS Ann."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, R., Yang, Y., and Yang, K. (2018, January 28\u201330). Small UAV based multi-viewpoint image registration for extracting the information of cultivated land in the hills and mountains. Proceedings of the 26th International Conference on Geoinformatics, Kunming, China.","DOI":"10.1109\/GEOINFORMATICS.2018.8557130"},{"key":"ref_12","unstructured":"Fernandez, P., Bartoli, A., and Davison, A. (2012, January 7\u201313). KAZE features. Proceedings of the 12th European Conference on Computer Vision, Florence, Italy."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent Advances in Convolutional Neural Networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_14","first-page":"102403","article-title":"Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration","volume":"102","author":"Meng","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","first-page":"678","article-title":"Descriptor matching with convolutional neural networks: A comparison to SIFT","volume":"4","author":"Fischer","year":"2014","journal-title":"Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nassar, A., Amer, K., ElHakim, R., and ElHelw, M. (2018, January 18\u201322). A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00201"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/LRA.2018.2809549","article-title":"Unsupervised deep homography: A fast and robust homography estimation model","volume":"3","author":"Nguyen","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/LGRS.2017.2781741","article-title":"Remote sensing image registration using convolutional neural network features","volume":"15","author":"Ye","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","first-page":"e63455","article-title":"Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain","volume":"10","author":"Wang","year":"2021","journal-title":"Nat. Libra Medic."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, R., Xu, F., Yu, H., Yang, W., and Li, H.C. (October, January 26). Edge-driven object matching for UAV images and satellite SAR images. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324021"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23168","DOI":"10.1109\/ACCESS.2021.3056701","article-title":"Energy-efficient and fast data collection in UAV-aided wireless sensor networks for Hilly Ter-Rains","volume":"9","author":"Nazib","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bae, W., Yoo, J., and Ye, J.C. (2017, January 21\u201326). Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.152"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"38544","DOI":"10.1109\/ACCESS.2018.2853100","article-title":"Multi-temporal remote sensing image registration using deep convolutional features","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ye, H., Su, K., and Huang, S. (2021, January 12\u201314). Image enhancement method based on bilinear interpolating and wavelet transform. Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9390624"},{"key":"ref_26","unstructured":"Chum, O., and Matas, J. (2005, January 21\u201323). Matching with PROSAC\u2014Progressive sample consensus. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_27","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"},{"key":"ref_28","first-page":"102274","article-title":"A framework for registering UAV-based imagery for crop-tracking in precision agriculture","volume":"97","author":"Jurado","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6353","DOI":"10.1109\/JSTARS.2021.3079404","article-title":"A generalized tool for accurate and efficient image registration of UAV multi-lens multispectral cameras by N-SURF matching","volume":"14","author":"Jhan","year":"2021","journal-title":"IEEE J. Sel. Top Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach","volume":"174","author":"Mohamed","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"023019","DOI":"10.1117\/1.JEI.25.2.023011","article-title":"Real-time tracking of deformable objects based on combined matching-and-tracking","volume":"25","author":"Yan","year":"2016","journal-title":"J. Electron. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9059","DOI":"10.1109\/TGRS.2019.2924684","article-title":"Fast and robust matching for multimodal remote sensing image registration","volume":"57","author":"Ye","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hou, X., Gao, Q., Wang, R., and Luo, X. (2021). Satellite-borne optical remote sensing image registration based on point features. Sensors, 21.","DOI":"10.3390\/s21082695"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3605\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:06Z","timestamp":1760166006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":33,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183605"],"URL":"https:\/\/doi.org\/10.3390\/rs13183605","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,10]]}}}