{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T12:29:54Z","timestamp":1768739394040,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61871413"],"award-info":[{"award-number":["61871413"]}]},{"name":"National Natural Science Foundation of China","award":["62171016"],"award-info":[{"award-number":["62171016"]}]},{"name":"National Natural Science Foundation of China","award":["62201027"],"award-info":[{"award-number":["62201027"]}]},{"name":"National Natural Science Foundation of China","award":["XK2020-03"],"award-info":[{"award-number":["XK2020-03"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["61871413"],"award-info":[{"award-number":["61871413"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62171016"],"award-info":[{"award-number":["62171016"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62201027"],"award-info":[{"award-number":["62201027"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["XK2020-03"],"award-info":[{"award-number":["XK2020-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR.<\/jats:p>","DOI":"10.3390\/rs14225842","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T04:08:40Z","timestamp":1668744520000},"page":"5842","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fast Line Segment Detection and Large Scene Airport Detection for PolSAR"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-6663","authenticated-orcid":false,"given":"Daochang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}]},{"given":"Qiang","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4906-6142","authenticated-orcid":false,"given":"Fei","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","first-page":"102425","article-title":"Employing deep learning for automatic river bridge detection from SAR images based on adaptively effective feature fusion","volume":"102","author":"Chen","year":"2021","journal-title":"Int. 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