{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T17:13:56Z","timestamp":1776532436424,"version":"3.51.2"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1711266"],"award-info":[{"award-number":["U1711266"]}],"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":["41925007"],"award-info":[{"award-number":["41925007"]}],"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":["41701429"],"award-info":[{"award-number":["41701429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/jstars.2020.3019410","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T20:26:12Z","timestamp":1598387172000},"page":"4973-4987","source":"Crossref","is-referenced-by-count":60,"title":["Semi-MCNN: A Semisupervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Submeter HRRS Images"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5259-5670","authenticated-orcid":false,"given":"Runyu","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5709-690X","authenticated-orcid":false,"given":"Ruyi","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-0845","authenticated-orcid":false,"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0680-5427","authenticated-orcid":false,"given":"Jining","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3005403"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.06.018"},{"key":"ref33","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"0","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Intervention"},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.3390\/rs9050498","article-title":"Classification for high resolution remote sensing imagery using a fully convolutional network","volume":"9","author":"fu","year":"2017","journal-title":"Remote Sens"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.11.011"},{"key":"ref30","article-title":"Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery","author":"sherrah","year":"2016"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2937830"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2890413"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2018.1453173"},{"key":"ref34","article-title":"Generative adversarial networks","author":"goodfellow","year":"2014"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1080\/014311698214235"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(97)00049-7"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2004.03.009"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0305-9006(03)00064-3"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/01431160500242515"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2016.2628406"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2009.06.002"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1080\/0143116031000114851"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1080\/02693799308901949"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(03)00132-9"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2011.568531"},{"key":"ref50","article-title":"Remote sensing image scene classification using CNN-CapsNet","volume":"11","author":"zhang","year":"2019","journal-title":"Remote Sens"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2873966"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2906883"},{"key":"ref10","first-page":"33","article-title":"Co-training and self-training for word sense disambiguation","author":"mihalcea","year":"0","journal-title":"Proc Computational Natural Language Learning"},{"key":"ref11","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.11.004"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2013.03.024"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.895416"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2780890"},{"key":"ref15","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"tong","year":"2018","journal-title":"Remote Sens Environ"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.07.001"},{"key":"ref17","article-title":"Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery","volume":"18","author":"noi","year":"2018","journal-title":"SENSORS"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2055876"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2009.11.002"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2011.03.009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2046(01)00160-8"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2393857"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2018.04.050"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2681128"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2909695"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/s18113717"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2685945"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2675998"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2783902"},{"key":"ref47","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","article-title":"Billion-scale semi-supervised learning for image classification","author":"yalniz","year":"2019"},{"key":"ref44","first-page":"122","article-title":"ShuffleNet V2: practical guidelines for efficient CNN architecture design","author":"ma","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/8994817\/09177262.pdf?arnumber=9177262","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T03:38:01Z","timestamp":1643168281000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9177262\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/jstars.2020.3019410","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}