{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:00:17Z","timestamp":1761490817837,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T00:00:00Z","timestamp":1563494400000},"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":["61603146, 41671454, and 41801386"],"award-info":[{"award-number":["61603146, 41671454, and 41801386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20160427 and BK20180797"],"award-info":[{"award-number":["BK20160427 and BK20180797"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Research in Colleges and Universities of Jiangsu Province","award":["16KJB520006"],"award-info":[{"award-number":["16KJB520006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.<\/jats:p>","DOI":"10.3390\/rs11141708","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T03:14:41Z","timestamp":1563506081000},"page":"1708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Shuang","family":"Cao","sequence":"first","affiliation":[{"name":"School of Remote Sensing &amp; Geomatics Engineering, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing &amp; Geomatics Engineering, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daifeng","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing &amp; Geomatics Engineering, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanqian","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Geographical Science, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/JSTARS.2010.2069555","article-title":"Automated vehicle extraction and speed determination from QuickBird satellite images","volume":"4","author":"Liu","year":"2011","journal-title":"IEEE J. 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