{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T13:52:33Z","timestamp":1782913953974,"version":"3.54.5"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,10]],"date-time":"2017-06-10T00:00:00Z","timestamp":1497052800000},"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":["61671480"],"award-info":[{"award-number":["61671480"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572486"],"award-info":[{"award-number":["61572486"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China)","award":["14CX02203A"],"award-info":[{"award-number":["14CX02203A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road detection plays key roles for remote sensing image analytics. Hough transform (HT) is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to develop a low computational complexity and time-saving Hough transform algorithm. In this paper, we propose a generalized Hough transform (i.e., Radon transform) implementation for road detection in remote sensing images. Specifically, we present a dictionary learning method to approximate the Radon transform. The proposed approximation method treats a Radon transform as a linear transform, which then facilitates parallel implementation of the Radon transform for multiple images. To evaluate the proposed algorithm, we conduct extensive experiments on the popular RSSCN7 database for straight road detection. The experimental results demonstrate that our method is superior to the traditional algorithms in terms of accuracy and computing complexity.<\/jats:p>","DOI":"10.3390\/rs9060590","type":"journal-article","created":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T10:27:59Z","timestamp":1497263279000},"page":"590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Road Detection by Using a Generalized  Hough Transform"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5388-9080","authenticated-orcid":false,"given":"Weifeng","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuying","family":"Li","sequence":"additional","affiliation":[{"name":"The 16th Institute, China Aerospace Science and Technology Corporation, Xi\u2019an 710100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dapeng","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, Kunming 650091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6710","DOI":"10.3390\/rs70606710","article-title":"Automated 3D scene reconstruction from open geospatial data sources: Airborne laser scanning and a 2D topographic database","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13921","DOI":"10.3390\/rs71013921","article-title":"Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud","volume":"7","author":"Cheng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Maboudi, M., Amini, J., Hahn, M., and Saati, M. 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