{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T01:43:36Z","timestamp":1778550216436,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Roads are closely related to people\u2019s lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and information flow transfer, and a residual dilated convolution module was introduced to extract road network information at different scales. The experimental results showed that the method proposed in this study outperformed the compared algorithms in overall accuracy. This method had fewer false detections, and the extracted roads were closer to ground truth. Ablation experiments showed that the proposed modules could effectively improve road extraction accuracy.<\/jats:p>","DOI":"10.3390\/rs14092061","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T02:14:39Z","timestamp":1650939279000},"page":"2061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Road Extraction Convolutional Neural Network with Embedded Attention Mechanism for Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Shiwei","family":"Shao","sequence":"first","affiliation":[{"name":"National Research Center of Cultural Industries, Central China Normal University, Wuhan 430056, China"},{"name":"Zhongzhi Software Technology Co., Ltd., Wuhan 430013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixia","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Wuhan Natural Resources and Planning Information Center, Wuhan 430014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liupeng","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1760-0865","authenticated-orcid":false,"given":"Chang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, J., Qin, Q., Gao, Z., Zhao, J., and Ye, X. 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