{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:06:56Z","timestamp":1783444016238,"version":"3.54.6"},"reference-count":11,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T00:00:00Z","timestamp":1562716800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningxia Hui Autonomous Region Key R&D Plan Projects","award":["2018YBZD1629"],"award-info":[{"award-number":["2018YBZD1629"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2019,7,10]]},"abstract":"<jats:p>Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.<\/jats:p>","DOI":"10.1155\/2019\/2373798","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T19:30:47Z","timestamp":1562787047000},"page":"1-9","source":"Crossref","is-referenced-by-count":37,"title":["Multiscale Road Extraction in Remote Sensing Images"],"prefix":"10.1155","volume":"2019","author":[{"given":"Aziguli","family":"Wulamu","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), No. 30 Xueyuan Road, Haidian District, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9973-8114","authenticated-orcid":true,"given":"Zuxian","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), No. 30 Xueyuan Road, Haidian District, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3456-5259","authenticated-orcid":true,"given":"Dezheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), No. 30 Xueyuan Road, Haidian District, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheyu","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), No. 30 Xueyuan Road, Haidian District, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2017.2699184"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"6","first-page":"1097","volume-title":"Imagenet classification with deep convolutional neural networks","year":"2012"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2644615"},{"key":"17","first-page":"3","volume-title":"U Net++: a nested U-Net architecture for medical image segmentation","year":"2018"},{"issue":"3","key":"21","first-page":"273","volume":"20","year":"1995","journal-title":"Machine Learning"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/lgrs.2018.2802944"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-015-0062-9"},{"key":"33","year":"2013"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/2373798.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/2373798.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/2373798.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T19:30:56Z","timestamp":1562787056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2019\/2373798\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,10]]},"references-count":11,"alternative-id":["2373798","2373798"],"URL":"https:\/\/doi.org\/10.1155\/2019\/2373798","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,10]]}}}