{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:51:31Z","timestamp":1760233891199,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Major Science and Technology Special Project of the Transportation Department of Jiangsu Province","award":["Z02"],"award-info":[{"award-number":["Z02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Aerial images are large-scale and susceptible to light. Traditional image feature point matching algorithms cannot achieve satisfactory matching accuracy for aerial images. This paper proposes a recursive diffusion algorithm, which is scale-invariant and can be used to extract symmetrical areas of different images. This narrows the matching range of feature points by extracting high-density areas of the image and improving the matching accuracy through correlation analysis of high-density areas. Through experimental comparison, it can be found that the recursive diffusion algorithm has more advantages compared to the correlation coefficient method and the mean shift algorithm when matching accuracy of aerial images, especially when the light of aerial images changes greatly.<\/jats:p>","DOI":"10.3390\/sym13030407","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T10:36:37Z","timestamp":1614681397000},"page":"407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0144-9067","authenticated-orcid":false,"given":"Jiayan","family":"Shen","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, No. 2 Dongnandaxue Road, Nanjing 211189, China"}]},{"given":"Xiucheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, No. 2 Dongnandaxue Road, Nanjing 211189, China"}]},{"given":"Wenzong","family":"Zhou","sequence":"additional","affiliation":[{"name":"ZTE Corporation, No. 55 Keji South Road, Shenzhen 518057, China"}]},{"given":"Yiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, No. 2 Dongnandaxue Road, Nanjing 211189, China"}]},{"given":"Juchen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, No. 2 Dongnandaxue Road, Nanjing 211189, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-319-28854-3_2","article-title":"Image Features Detection, Description and Matching","volume":"Volume 630","author":"Awad","year":"2016","journal-title":"Image Feature Detectors and Descriptors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, L., Rottensteiner, F., and Heipke, C. (2020). Feature detection and description for image matching: From hand-crafted design to deep learning. Geo-Spat. Inf. Sci.","DOI":"10.1080\/10095020.2020.1843376"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object Recognition from Local Scale-Invariant Features. Proceedings of the IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s11263-006-0002-3","article-title":"Automatic panoramic image stitching using invariant features","volume":"74","author":"Brown","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","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 IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.inffus.2014.05.004","article-title":"Multi-focus image fusion with dense SIFT","volume":"23","author":"Liu","year":"2015","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shi, H.T., Guo, L., Tan, S., Li, G., and Sun, J. (2019). Improved Parallax Image Stitching Algorithm Based on Feature Block. Symmetry, 11.","DOI":"10.3390\/sym11030348"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106323","DOI":"10.1016\/j.optlaseng.2020.106323","article-title":"3D SIFT aided path independent digital volume correlation and its GPU acceleration","volume":"136","author":"Yang","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11263-008-0195-8","article-title":"Nonlinear Mean shift over Riemannian Manifolds","volume":"84","author":"Subbarao","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1109\/TPAMI.2013.190","article-title":"Semi-Supervised Kernel Mean Shift Clustering","volume":"36","author":"Anand","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7448","DOI":"10.1109\/TGRS.2014.2312793","article-title":"Road Centerline Extraction in Complex Urban Scenes From LiDAR Data Based on Multiple Features","volume":"52","author":"Hu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15003","DOI":"10.1007\/s11042-017-5085-z","article-title":"Research on multi-camera information fusion method for intelligent perception","volume":"77","author":"Feng","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yue, L.W., Li, H.J., and Zheng, X.W. (2019). Distorted Building Image Matching with Automatic Viewpoint Rectification and Fusion. Sensors, 19.","DOI":"10.3390\/s19235205"},{"key":"ref_18","unstructured":"Duraisamy, P., and Jackson, S. (2013, January 20\u201321). Orthogonal Detection and Registration for Microstructures Images. Proceedings of the IEEE International Conference on Communication and Computer Vision, Coimbatore, India."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Won, I., Jeong, J., Yang, H., Kwon, J., and Jeong, D. (2016). Adaptive Image Matching Using Discrimination of Deformable Object. Symmetry, 8.","DOI":"10.3390\/sym8070068"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jeong, J., Won, I., Yang, H., Lee, B., and Jeong, D. (2017). Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering. Symmetry, 9.","DOI":"10.3390\/sym9020025"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/TGRS.2015.2441954","article-title":"Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming","volume":"53","author":"Ma","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1109\/TGRS.2018.2820040","article-title":"Guided Locality Preserving Feature Matching for Remote Sensing Image Registration","volume":"56","author":"Ma","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.1007\/s11042-018-6266-0","article-title":"2-Levels of clustering strategy to detect and locate copy-move forgery in digital images","volume":"79","author":"Manogaran","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., and Moreno-Noguer, F. (2015, January 11\u201318). Discriminative Learning of Deep Convolutional Feature Point Descriptors. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.22"},{"key":"ref_26","unstructured":"Liu, Z.S., Li, Z.X., Zhang, J.Y., and Liu, L.G. (July, January 26). Euclidean and Hamming Embedding for Image Patch Description with Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA."},{"key":"ref_27","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_28","unstructured":"Wang, R.Z., Yan, J.C., and Yang, X.K. (November, January 27). Learning Combinatorial Embedding Networks for Deep Graph Matching. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kluger, F., Brachmann, E., Ackermann, H., Rother, C., Yang, M.Y., and Rosenhahn, B. (2020, January 13\u201319). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00469"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/3\/407\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:31:31Z","timestamp":1760160691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/3\/407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["sym13030407"],"URL":"https:\/\/doi.org\/10.3390\/sym13030407","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,3,2]]}}}