{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:44:01Z","timestamp":1761129841068,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"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":["61673017","61403398"],"award-info":[{"award-number":["61673017","61403398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2017JM6077","2018ZDXM-GY-039"],"award-info":[{"award-number":["2017JM6077","2018ZDXM-GY-039"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs13030490","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T06:22:20Z","timestamp":1611987740000},"page":"490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fast Aerial Image Geolocalization Using the Projective-Invariant Contour Feature"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0282-3073","authenticated-orcid":false,"given":"Yongfei","family":"Li","sequence":"first","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"He","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1294-8283","authenticated-orcid":false,"given":"Deyu","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4948-4056","authenticated-orcid":false,"given":"Dongfang","family":"Yang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Hi-Tech, Xi\u2019an 710025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, H., Yang, D., Wang, S., Wang, S., and Li, Y. (2019). Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss. Remote Sens., 11.","DOI":"10.3390\/rs11091015"},{"key":"ref_2","unstructured":"Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C., and Paluri, M. (November, January 27). Improved road connectivity by joint learning of orientation and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE), Seoul, Korea."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., and DeWitt, D. (2018, January 18\u201322). Roadtracer: Automatic extraction of road networks from aerial images. 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