{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:08:37Z","timestamp":1775470117464,"version":"3.50.1"},"reference-count":102,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1109\/tgrs.2018.2838665","type":"journal-article","created":{"date-parts":[[2018,6,20]],"date-time":"2018-06-20T18:40:42Z","timestamp":1529520042000},"page":"6440-6461","source":"Crossref","is-referenced-by-count":259,"title":["Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach"],"prefix":"10.1109","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6701-961X","authenticated-orcid":false,"given":"Juan Mario","family":"Haut","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1030-3729","authenticated-orcid":false,"given":"Mercedes E.","family":"Paoletti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-9141","authenticated-orcid":false,"given":"Javier","family":"Plaza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-9448","authenticated-orcid":false,"given":"Jun","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref33","article-title":"Statistical methods and neural network approaches for classification of data from multiple sources","author":"benediktsson","year":"1990"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1080\/014311699213622"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1080\/01431169308904316"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1080\/014311697218700"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2584107"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1561\/2000000039"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref34","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref28","first-page":"1625","article-title":"Yinyang K-means clustering for hyperspectral image analysis","author":"paoletti","year":"2017","journal-title":"Proc 17th Int Conf Comput Math Methods Sci Eng"},{"key":"ref27","author":"ball","year":"1965","journal-title":"ISODATA A novel method of data analysis and pattern classification"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","author":"bishop","year":"1995","journal-title":"Neural Networks for Pattern Recognition"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2008.4779668"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2007.07.028"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2008.4779667"},{"key":"ref24","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","author":"macqueen","year":"1967","journal-title":"Proc 5th Berkeley Symp Math Statist Probability"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12164"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2009.2016214"},{"key":"ref100","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-016-1896-3"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised spectral–spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification","volume":"12","author":"tao","year":"2015","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2014.2309695"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.3390\/rs8020099"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127331"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.010"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/258619"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2772836"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3390\/rs9030298"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301268"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2636241"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.063"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(98)00064-9"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.1989.579044"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1029\/1999JD900098"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730182"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1080\/01431160802282854"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2329330"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1117\/12.943611"},{"key":"ref9","first-page":"836004-1","article-title":"Airborne infrared hyperspectral imager for intelligence, surveillance, and reconnaissance applications","volume":"8360","author":"puckrin","year":"2012","journal-title":"Proc SPIE"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2675902"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1007\/978-3-319-27857-5_61","article-title":"A deep belief network for classifying remotely-sensed hyperspectral data","author":"le","year":"2015","journal-title":"Advances in Visual Computing"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326943"},{"key":"ref47","first-page":"1","article-title":"Hyperspectral classification using stacked autoencoders with deep learning","author":"okan","year":"2014","journal-title":"Proc 6th Workshop Hyperspectral Image Signal Process Evol Remote Sens (WHISPERS)"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7026039"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2388577"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2060550"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.910220"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2010404"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.4.590"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1590\/S0104-65001997000200006"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.448"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2011.2139193"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2205263"},{"key":"ref78","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-0745-0","author":"neal","year":"1996","journal-title":"Bayesian learning for neural networks"},{"key":"ref79","article-title":"Uncertainty in Deep Learning","author":"gal","year":"2016"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730324"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2755542"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2017.1280200"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.3390\/rs9010067"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.11.021"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1032181158"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2006.04.001"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2296031"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054102"},{"key":"ref2","author":"lucas","year":"2004","journal-title":"Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1002\/0470124628"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.228.4704.1147"},{"key":"ref95","author":"liu","year":"2016","journal-title":"Adaptive deep pyramid matching for remote sensing scene classification"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2128330"},{"key":"ref93","first-page":"478","article-title":"Active learning to recognize multiple types of plankton","volume":"3","author":"luo","year":"2004","journal-title":"Proc 17th Int Conf Pattern Recognit (ICPR)"},{"key":"ref92","author":"houlsby","year":"2011","journal-title":"Bayesian active learning for classification and preference learning"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb00917.x"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2016.90"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127319"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.2307\/2006193"},{"key":"ref99","first-page":"1063","article-title":"Cloud implementation of logistic regression for hyperspectral image classification","author":"haut","year":"2017","journal-title":"Proc 17th Int Conf Comput Math Methods Sci Eng (CMMSE)"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.01.004"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.02.013"},{"key":"ref10","first-page":"51","article-title":"The hyperspectral sensors used in satellite and aerial remote sensing","volume":"2","author":"vorovencii","year":"2009","journal-title":"Bull Transilvania Univ Bra?ov"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1117\/3.673407"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.21236\/ADA430883"},{"key":"ref13","author":"chang","year":"2003","journal-title":"Hyperspectral Imaging Techniques for Spectral Detection and Classification"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2616418"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/RAST.2013.6581194"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2011.940409"},{"key":"ref82","article-title":"Active learning for high dimensional inputs using Bayesian convolutional neural networks","author":"islam","year":"2016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2362959"},{"key":"ref81","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume":"48","author":"gal","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/2193-1801-3-130","article-title":"ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration","volume":"3","author":"pour","year":"2014","journal-title":"SpringerPlus"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2540798"},{"key":"ref19","first-page":"117","author":"abrams","year":"2013","journal-title":"NASA’s Hyperspectral Infrared Imager (HyspIRI)"},{"key":"ref83","article-title":"Bayesian convolutional neural networks with Bernoulli approximate variational inference","author":"gal","year":"2016","journal-title":"Proc 4th Int Conf Learn Represent (ICLR) Workshop Track"},{"key":"ref80","first-page":"853","article-title":"Transforming neural-net output levels to probability distributions","author":"denker","year":"1990","journal-title":"Proc 3rd Conf Adv Neural Inf Process Syst (NIPS-3)"},{"key":"ref89","article-title":"Deep Bayesian active learning with image data","author":"gal","year":"2016","journal-title":"Workshop on Bayesian Deep Learning NIPS"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"ref86","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref87","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist (AISTATS)"},{"key":"ref88","first-page":"1613","article-title":"Weight uncertainty in neural networks","volume":"37","author":"blundell","year":"2015","journal-title":"Proc Int Conf Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/8509678\/08390931.pdf?arnumber=8390931","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T16:29:00Z","timestamp":1720369740000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8390931\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":102,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tgrs.2018.2838665","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11]]}}}