{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T08:15:25Z","timestamp":1787818525297,"version":"build-2784847793"},"reference-count":18,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2017,3]]},"abstract":"<jats:p> Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral\u2013spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN). <\/jats:p>","DOI":"10.1142\/s1469026817500018","type":"journal-article","created":{"date-parts":[[2017,3,14]],"date-time":"2017-03-14T02:16:53Z","timestamp":1489457813000},"page":"1750001","source":"Crossref","is-referenced-by-count":93,"title":["Convolutional Neural Networks for Water Body Extraction from Landsat Imagery"],"prefix":"10.1142","volume":"16","author":[{"given":"Long","family":"Yu","sequence":"first","affiliation":[{"name":"Network Center, Xinjiang University, 14 Shengli Road, Urumqi, Xinjiang 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, 499 Xibei Road, Urumqi, Xinjiang 830008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengwei","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, 499 Xibei Road, Urumqi, Xinjiang 830008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feiyue","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cloud Computing and Intelligent Information, Processing of Changzhou City, Jiangsu University of Technology, Changzhou, Jiangsu 213000, China"},{"name":"School of Computer Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianli","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Resource and Environment Sciences, Xinjiang University, 14 Shengli Road, Urumqi, Xinjiang 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2017,3,23]]},"reference":[{"issue":"6","key":"S1469026817500018BIB001","first-page":"479","volume":"19","author":"Chen H. 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