{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:15:11Z","timestamp":1779135311259,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-source image registration based on geographical location information (GLI). By calculating the geographic location information that corresponds to the pixel in the image, the ideal projected pixel position of the corresponding image is obtained using spatial coordinate transformation. Additionally, the corresponding relationship between the two images is calculated by combining multiple sets of registration points. The simulation experiment illustrates that, under selected common simulation parameters, the average value of the relative registration-point error between the two images is 12.64 pixels, and the registration accuracy of the corresponding ground registration point is higher than 6.5 m. In the registration experiment involving remote sensing images from different sources, the average registration pixel error of this algorithm is 20.92 pixels, and the registration error of the image center is 21.24 pixels. In comparison, the image center registration error given by the convolutional neural network (CNN) is 142.35 pixels after the registration error is manually eliminated. For the registration of homologous and featureless remote sensing images, the SIFT algorithm can only offer one set of registration points for the correct region, and the neural network cannot achieve accurate registration results. The registration accuracy of the presented algorithm is 7.2 pixels, corresponding to a ground registration accuracy of 4.32 m and achieving more accurate registration between featureless images.<\/jats:p>","DOI":"10.3390\/rs15020436","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T05:26:31Z","timestamp":1673414791000},"page":"436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Image Registration Algorithm for Remote Sensing Images Based on Pixel Location Information"],"prefix":"10.3390","volume":"15","author":[{"given":"Xuming","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electronic Science and Engineering, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5502-4689","authenticated-orcid":false,"given":"Yao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space-Based Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Qiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space-Based Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoning","family":"Lv","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space-Based Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space-Based Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science, Faculty of Physics and Applied Sciences, University of Southampton, Southampton SO17 1BJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4939-1108","authenticated-orcid":false,"given":"Yiming","family":"Cai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space-Based Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2014.01.009","article-title":"A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences","volume":"90","author":"Ye","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4477","DOI":"10.1080\/01431161.2022.2114112","article-title":"Feature based remote sensing image registration techniques: A comprehensive and comparative review","volume":"43","author":"Misra","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wu, S., Zhong, R., Li, Q., Qiao, K., and Zhu, Q. (2021). An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering. Remote Sens., 13.","DOI":"10.3390\/rs13081491"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"706168","DOI":"10.1117\/12.7971508","article-title":"Digital Registration of Multispectral Video Imagery","volume":"7","author":"Anuta","year":"1969","journal-title":"Opt. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TC.1972.5008923","article-title":"A class of algorithms for fast digital image registration","volume":"100","author":"Barnea","year":"1972","journal-title":"IEEE Trans. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0734-189X(89)80014-3","article-title":"Multiresolution Elastic Matching","volume":"46","author":"Bajcs","year":"1989","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Roche, A., Malandain, G., Pennec, X., and Ayache, N. (1998). The correlation ratio as a new similarity measure for multimodal image registration. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Springer.","DOI":"10.1007\/BFb0056301"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/42.563664","article-title":"Multimodality image registration by maximization of mutual information","volume":"16","author":"Maes","year":"1997","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/TGRS.2013.2242895","article-title":"Automatic registration of multisensor images using an integrated spatial and mutual information (SMI) metric","volume":"52","author":"Liang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.1109\/TGRS.2011.2144607","article-title":"Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images","volume":"49","author":"Sedaghat","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/LGRS.2016.2582528","article-title":"Remote Sensing Optical Image Registration Using Modified Uniform Robust SIFT","volume":"13","author":"Paul","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, X., Zhao, L., and Chen, S. (2019). A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation. Remote Sens., 11.","DOI":"10.3390\/rs11151833"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gong, X., Yao, F., Ma, J., Jiang, J., Lu, T., Zhang, Y., and Zhou, H. (2022). Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency. Remote Sens., 14.","DOI":"10.3390\/rs14112606"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Gool, L.V. (2006, January 7\u201313). SURF: Speeded up robust features. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-Like Algorithm for SAR Images","volume":"53","author":"Dellinger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TGRS.2015.2420659","article-title":"Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor","volume":"53","author":"Sedaghat","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/LGRS.2015.2507982","article-title":"Feature-Area Optimization: A Novel SAR Image Registration Method","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, L., Han, L., and Ye, Y. (2022). Self-Supervised Keypoint Detection and Cross-Fusion Matching Networks for Multimodal Remote Sensing Image Registration. Remote Sens., 14.","DOI":"10.3390\/rs14153599"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2017.12.012","article-title":"A deep learning framework for remote sensing image registration","volume":"145","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Maggiolo, L., Solarna, D., Moser, G., and Serpico, S.B. (2022). Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure. Remote Sens., 14.","DOI":"10.3390\/rs14122811"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Feng, R., Li, X., Bai, J., and Ye, Y. (2022). MID: A Novel Mountainous Remote Sensing Imagery Registration Dataset Assessed by a Coarse-to-Fine Unsupervised Cascading Network. Remote Sens., 14.","DOI":"10.3390\/rs14174178"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22758","DOI":"10.1038\/srep22758","article-title":"Precession andnutation dynamics of nonlinearly coupled non-coaxial three-dimensional matter wave vortices","volume":"6","author":"Driben","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_25","first-page":"2205","article-title":"Keplerian orbit elements induced by precession, nutation and polar motion","volume":"34","author":"Zhang","year":"2019","journal-title":"Prog. Geophys."},{"key":"ref_26","first-page":"340","article-title":"Attitude Planning and Fast Simulation Method of Optical Remote Sensing Satellite Staring Imaging","volume":"51","author":"Zhang","year":"2021","journal-title":"J. Jilin Univ. (Eng. Ed.)"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ioannidou, S., and Pantazis, G. (2020). Helmert Transformation Problem. From Euler Angles Method to Quaternion Algebra. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090494"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"74865","DOI":"10.1109\/ACCESS.2018.2882502","article-title":"A Method of Robot Base Frame Calibration by Using Dual Quaternion Algebra","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"084031","DOI":"10.1103\/PhysRevD.88.084031","article-title":"Precession-tracking coordinates for simulations of compact-object binaries","volume":"88","author":"Ossokine","year":"2013","journal-title":"Phys. Rev. D"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/436\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:03:05Z","timestamp":1760119385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020436"],"URL":"https:\/\/doi.org\/10.3390\/rs15020436","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]}}}