{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:06:35Z","timestamp":1760709995392,"version":"3.37.3"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFB0505000"],"award-info":[{"award-number":["2018YFB0505000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2938215","type":"journal-article","created":{"date-parts":[[2019,8,30]],"date-time":"2019-08-30T00:07:40Z","timestamp":1567123660000},"page":"122784-122795","source":"Crossref","is-referenced-by-count":8,"title":["Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-8831","authenticated-orcid":false,"given":"Panle","family":"Li","sequence":"first","affiliation":[]},{"given":"Xiaohui","family":"He","sequence":"additional","affiliation":[]},{"given":"Xijie","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Runchuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mengjia","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Daidong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fangbing","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Zhiqiang","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1145\/2775054.2694373","article-title":"A probabilistic graphical model-based approach for minimizing energy under performance constraints","volume":"50","author":"mishra","year":"2015","journal-title":"ACM SIGPLAN Notices"},{"key":"ref32","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","author":"goldberger","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref31","first-page":"10456","article-title":"Using trusted data to train deep networks on labels corrupted by severe noise","author":"hendrycks","year":"2018","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.696"},{"key":"ref37","article-title":"Convolutional neural network for road extraction","volume":"10605","author":"li","year":"2017","journal-title":"Proc SPIE"},{"key":"ref36","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730322"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2802944"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_19"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.168"},{"key":"ref12","first-page":"1919","article-title":"Robust loss functions under label noise for deep neural networks","author":"ghosh","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref13","article-title":"Auxiliary image regularization for deep cnns with noisy labels","author":"azadi","year":"2015","journal-title":"arXiv 1511 07069"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref15","first-page":"1196","article-title":"Learning with noisy labels","author":"natarajan","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","first-page":"233","article-title":"A closer look at memorization in deep networks","volume":"70","author":"arpit","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref17","first-page":"1","article-title":"Multiple object extraction from aerial imagery with convolutional neural networks","volume":"60","author":"saito","year":"2016","journal-title":"Electron Imag"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2672734"},{"article-title":"Machine learning for aerial image labeling","year":"2013","author":"mnih","key":"ref19"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-annals-IV-1-W1-215-2017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2734697"},{"key":"ref27","first-page":"133","article-title":"Using label noise robust logistic regression for automated updating of topographic geospatial databases","volume":"3","author":"maas","year":"2016","journal-title":"Proc 23rd ISPRS Congr Commission VIII"},{"key":"ref3","article-title":"A multi-stage multi-task neural network for aerial scene interpretation and geolocalization","author":"marcu","year":"2018","journal-title":"arXiv 1804 01322"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.199"},{"key":"ref29","first-page":"2691","article-title":"Learning from massive noisy labeled data for image classification","author":"xiao","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref5","first-page":"1","article-title":"Land-use characterisation using Google Street View pictures and OpenStreetMap","author":"srivastava","year":"2018","journal-title":"Proc Assoc Geographic Inf Laboratories Eur Conf (AGILE)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.11.010"},{"key":"ref7","first-page":"210","article-title":"Learning to detect roads in high-resolution aerial images","author":"mnih","year":"2010","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2782260"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/rs9020173"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2750680"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472164"},{"key":"ref21","first-page":"567","article-title":"Learning to label aerial images from noisy data","author":"mnih","year":"2012","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","article-title":"Training convolutional networks with noisy labels","author":"sukhbaatar","year":"2014","journal-title":"arXiv 1406 2080"},{"key":"ref23","first-page":"514","article-title":"Convolutional Neural Network-based SAR Image Classification with Noisy Labels","volume":"6","author":"zhao","year":"2017","journal-title":"J Radars"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.14358\/PERS.84.5.263"},{"key":"ref25","article-title":"Learning from noisy labels with deep neural networks","author":"sukhbaatar","year":"2015","journal-title":"arXiv 1406 2080"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08819957.pdf?arnumber=8819957","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T23:11:54Z","timestamp":1643238714000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8819957\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2938215","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}