{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:50:38Z","timestamp":1780919438858,"version":"3.54.1"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T00:00:00Z","timestamp":1533081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1109\/jstars.2018.2849363","type":"journal-article","created":{"date-parts":[[2018,8,27]],"date-time":"2018-08-27T23:50:31Z","timestamp":1535413831000},"page":"2615-2629","source":"Crossref","is-referenced-by-count":130,"title":["Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-3583","authenticated-orcid":false,"given":"Ksenia","family":"Bittner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fathalrahman","family":"Adam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5417-4482","authenticated-orcid":false,"given":"Shiyong","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9186-4175","authenticated-orcid":false,"given":"Marco","family":"Korner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2015.1066527"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/978-3-642-46466-9_18","article-title":"Neocognitron: A self-organizing neural network model for a mechanism of visual pattern\n recognition","author":"fukushima","year":"1982","journal-title":"Competition and Cooperation in Neural Nets"},{"key":"ref38","article-title":"Building\n footprint extraction from digital surface models using neural networks","volume":"10004","author":"davydova","year":"2016","journal-title":"Proc SPIE"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2007.03.001"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1080\/19479832.2013.848477"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2010.5548052"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2002.800629"},{"key":"ref37","article-title":"Machine learning for aerial image labeling","author":"mnih","year":"2013"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2011.2179792"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1117\/12.806141"},{"key":"ref34","volume":"1","author":"shafer","year":"1976","journal-title":"et\ufffdal"},{"key":"ref60","article-title":"Artificial generation of big data for\n improving image classification: A generative adversarial network approach on SAR data","author":"marmanis","year":"2017","journal-title":"arXiv 1711 02010"},{"key":"ref62","first-page":"315","article-title":"Deep sparse\n rectifier neural networks","author":"glorot","year":"0","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref61","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref63","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-015-3177-1"},{"key":"ref64","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(02)00241-5"},{"key":"ref65","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"tieleman","year":"2012","journal-title":"Neural Netw Mach Learning"},{"key":"ref66","article-title":"An overview of gradient descent optimization algorithms","author":"ruder","year":"2016","journal-title":"arXiv 1609 04747"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/5.554205"},{"key":"ref67","article-title":"What are the receptive, effective receptive, and projective fields of neurons in convolutional neural\n networks?","author":"le","year":"2017","journal-title":"arXiv 1705 07049"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.5194\/isprsarchives-XXXVIII-4-W19-79-2011"},{"key":"ref69","doi-asserted-by":"crossref","DOI":"10.1117\/12.917801","article-title":"Fusing stereo and multispectral data\n from WorldView-2 for urban modeling","volume":"8390","author":"krau\u00df","year":"2012","journal-title":"Proc SPIE"},{"key":"ref2","first-page":"345","article-title":"Extraction of buildings from high resolution satellite data","author":"sohn","year":"2001","journal-title":"Automatic Extraction of Man-Made Objects from Aerial and Space Images (III)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2010.02.001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.17562\/PB-51-2"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.4304\/jmm.9.1.181-188"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/819768"},{"key":"ref24","doi-asserted-by":"crossref","DOI":"10.1201\/9781420053098","author":"liggins","year":"2017","journal-title":"Handbook of Multisensor Data Fusion Theory and Practice"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2015.7351047"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2009.934822"},{"key":"ref25","first-page":"1","article-title":"Enhanced\n self-organizing map for passive sonar tracking to improve situation awareness","author":"lai","year":"0","journal-title":"Proc 10th Int Conf Inf Fusion"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326745"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.5194\/isprsannals-III-3-473-2016"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7729468"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/JURSE.2017.7924565"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.11.009"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref54","first-page":"180","article-title":"Semantic segmentation of earth observation data using multimodal and multi-scale deep networks","author":"audebert","year":"0","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref53","article-title":"DeepLab: Semantic image segmentation with deep convolutional nets,\n atrous convolution, and fully connected CRFs","author":"chen","year":"2016","journal-title":"arXiv 1606 00915"},{"key":"ref52","article-title":"Fully convolutional networks for dense semantic labelling of\n high-resolution aerial imagery","author":"sherrah","year":"2016","journal-title":"arXiv 1606 02585"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.14358\/PERS.74.2.215"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/0734-189X(88)90016-3"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/21.44071"},{"key":"ref16","first-page":"1","article-title":"Building footprint simplification based on Hough transform and least squares adjustment","author":"guercke","year":"0","journal-title":"Proc 14th Workshop ICA Commission Generalisation Multiple Represent"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3138\/FM57-6770-U75U-7727"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2002027"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2011.2178399"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2011.2168195"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2004.10.006"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1998.0724"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2013.09.004"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2012.11.007"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1999.0803"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-archives-XLII-1-W1-481-2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2007.01.001"},{"key":"ref46","first-page":"291","article-title":"HF-FCN: Hierarchically fused\n fully convolutional network for robust building extraction","author":"zuo","year":"0","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref45","article-title":"Automatic building extraction in aerial scenes using convolutional\n networks","author":"yuan","year":"2016","journal-title":"arXiv 1602 06564"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2612821"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.231"},{"key":"ref41","first-page":"656","article-title":"Convolutional-recursive deep learning for 3D object classification","author":"socher","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref43","first-page":"234","article-title":"U-Net:\n Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"0","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Intervention"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/8445680\/08447548.pdf?arnumber=8447548","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T15:23:10Z","timestamp":1643210590000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8447548\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":71,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/jstars.2018.2849363","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}