{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:53:47Z","timestamp":1784904827545,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,2]],"date-time":"2023-07-02T00:00:00Z","timestamp":1688256000000},"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":["62173320"],"award-info":[{"award-number":["62173320"]}],"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>Feature matching is a fundamental task in the field of image processing, aimed at ensuring correct correspondence between two sets of features. Putative matches constructed based on the similarity of descriptors always contain a large number of false matches. To eliminate these false matches, we propose a remote sensing image feature matching method called LMC (local motion consistency), where local motion consistency refers to the property that adjacent correct matches have the same motion. The core idea of LMC is to find neighborhoods with correct motion trends and retain matches with the same motion. To achieve this, we design a local geometric constraint using a homography matrix to represent local motion consistency. This constraint has projective invariance and is applicable to various types of transformations. To avoid outliers affecting the search for neighborhoods with correct motion, we introduce a resampling method to construct neighborhoods. Moreover, we design a jump-out mechanism to exit the loop without searching all possible cases, thereby reducing runtime. LMC can process over 1000 putative matches within 100 ms. Experimental evaluations on diverse image datasets, including SUIRD, RS, and DTU, demonstrate that LMC achieves a higher F-score and superior overall matching performance compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs15133379","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"3379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Homography Matrix-Based Local Motion Consistent Matching for Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9586-0915","authenticated-orcid":false,"given":"Junyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5969-4022","authenticated-orcid":false,"given":"Ao","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingqi","family":"Pang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,2]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Li, X., Luo, X., Wu, Y., Li, Z., and Xu, W. (October, January 26). Research on stereo matching for satellite generalized image pair based on improved SURF and RFM. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324216"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/MGRS.2021.3122248","article-title":"Unmanned aerial vehicle-based photogrammetric 3d mapping: A survey of techniques, applications, and challenges","volume":"10","author":"Jiang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3157870","article-title":"Robust feature matching for remote sensing image registration via guided hyperplane fitting","volume":"60","author":"Xiao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"Deep feature correlation learning for multi-modal remote sensing image registration","volume":"60","author":"Quan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, C.C., Pankanti, S.U., Natesan Ramamurthy, K., and Aravkin, A.Y. (2015, January 7\u201312). Adaptive as-natural-as-possible image stitching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298719"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1109\/LGRS.2020.3008400","article-title":"Fast unmanned aerial vehicle image matching combining geometric information and feature similarity","volume":"18","author":"Wei","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","unstructured":"Wang, C., Wang, L., and Liu, L. (2014). Computer Vision\u2014ECCV 2014, Proceedings of the 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014, Springer. Proceedings, Part II 13."},{"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":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1006\/cviu.1999.0832","article-title":"MLESAC: A new robust estimator with application to estimating image geometry","volume":"78","author":"Torr","year":"2000","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_15","unstructured":"Chum, O., and Matas, J. (2005, January 20\u201325). Matching with PROSAC-progressive sample consensus. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Barath, D., Noskova, J., Ivashechkin, M., and Matas, J. (2020, January 13\u201319). MAGSAC++, a fast, reliable and accurate robust estimator. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00138"},{"key":"ref_17","first-page":"4961","article-title":"Graph-cut RANSAC: Local optimization on spatially coherent structures","volume":"44","author":"Barath","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11263-010-0318-x","article-title":"Rejecting mismatches by correspondence function","volume":"89","author":"Li","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","article-title":"Point set registration: Coherent point drift","volume":"32","author":"Myronenko","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, J., Ma, J., Tian, J., Ma, J., and Zhang, D. (2011, January 20\u201325). A robust method for vector field learning with application to mismatch removing. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995336"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, H., and Yan, S. (2010, January 13\u201318). Common visual pattern discovery via spatially coherent correspondences. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539780"},{"key":"ref_22","unstructured":"Zhou, F., and De la Torre, F. (2012, January 16\u201321). Factorized graph matching. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TPAMI.2013.223","article-title":"Gnccp\u2014Graduated nonconvexityand concavity procedure","volume":"36","author":"Liu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.ejor.2005.09.032","article-title":"A survey for the quadratic assignment problem","volume":"176","author":"Loiola","year":"2007","journal-title":"Eur. J. Oper. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108588","DOI":"10.1016\/j.patcog.2022.108588","article-title":"Robust image matching via local graph structure consensus","volume":"126","author":"Jiang","year":"2022","journal-title":"Pattern Recogn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1007\/s11263-022-01644-2","article-title":"Feature matching via motion-consistency driven probabilistic graphical model","volume":"130","author":"Ma","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1109\/TIP.2019.2906490","article-title":"LMR: Learning a two-class classifier for mismatch removal","volume":"28","author":"Ma","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., and Fua, P. (2018, January 8\u201314). Learning to find good correspondences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Munich, Germany.","DOI":"10.1109\/CVPR.2018.00282"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020, January 13\u201319). Superglue: Learning feature matching with graph neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.isprsjprs.2022.06.009","article-title":"Learning for mismatch removal via graph attention networks","volume":"190","author":"Jiang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1109\/TIP.2022.3166284","article-title":"Csr-net: Learning adaptive context structure representation for robust feature correspondence","volume":"31","author":"Chen","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108167","DOI":"10.1016\/j.patcog.2021.108167","article-title":"GLMNet: Graph learning-matching convolutional networks for feature matching","volume":"121","author":"Jiang","year":"2022","journal-title":"Pattern Recogn."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Rahmani, H., Angelov, P., Black, S., and Williams, B.M. (2022, January 18\u201324). Graph-context Attention Networks for Size-varied Deep Graph Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00238"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1007\/s11263-018-1117-z","article-title":"Locality preserving matching","volume":"127","author":"Ma","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bian, J., Lin, W.Y., Matsushita, Y., Yeung, S.K., Nguyen, T.D., and Cheng, M.M. (2017, January 21\u201326). Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.302"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/LGRS.2020.2999438","article-title":"A discriminative point matching algorithm based on local structure consensus constraint","volume":"18","author":"Shao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6462","DOI":"10.1109\/TGRS.2019.2906183","article-title":"Multiscale locality and rank preservation for robust feature matching of remote sensing images","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, J., Yang, M., Gong, W., and Yu, Y. (2022). Multi-neighborhood Guided Kendall Rank Correlation Coefficient for Feature Matching. IEEE Trans. Multimed.","DOI":"10.1109\/TMM.2022.3217410"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2021.11.004","article-title":"Robust feature matching via neighborhood manifold representation consensus","volume":"183","author":"Ma","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.isprsjprs.2022.08.015","article-title":"A frame-based probabilistic local verification method for robust correspondence","volume":"192","author":"Shen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"1","article-title":"Local Affine Preservation with Motion Consistency for Feature Matching of Remote Sensing Images","volume":"60","author":"Ye","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K. (2017, January 21\u201326). HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.410"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s11263-016-0902-9","article-title":"Large-scale data for multiple-view stereopsis","volume":"120","author":"Jensen","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","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":"Inform. Fusion"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3379\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:04:50Z","timestamp":1760126690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,2]]},"references-count":45,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133379"],"URL":"https:\/\/doi.org\/10.3390\/rs15133379","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,2]]}}}