{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:12:48Z","timestamp":1775913168011,"version":"3.50.1"},"reference-count":159,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","funder":[{"DOI":"10.13039\/501100014181","name":"Junta de Extremadura","doi-asserted-by":"publisher","award":["GR18060"],"award-info":[{"award-number":["GR18060"]}],"id":[{"id":"10.13039\/501100014181","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Spanish Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2019-110315RB-I00"],"award-info":[{"award-number":["PID2019-110315RB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program","award":["734541"],"award-info":[{"award-number":["734541"]}]},{"name":"Computing Facilities of Extremadura Research Centre for Advanced Technologies"},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund (ERDF); CETA-CIEMAT belongs to CIEMAT and the Government of Spain","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. IEEE"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1109\/jproc.2021.3063258","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T20:04:38Z","timestamp":1615838678000},"page":"1320-1349","source":"Crossref","is-referenced-by-count":47,"title":["Distributed Deep Learning for Remote Sensing Data Interpretation"],"prefix":"10.1109","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6701-961X","authenticated-orcid":false,"given":"Juan M.","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\/0000-0002-1858-9920","authenticated-orcid":false,"given":"Sergio","family":"Moreno-Alvarez","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\/0000-0002-4264-7473","authenticated-orcid":false,"given":"Juan-Antonio","family":"Rico-Gallego","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":"10.1117\/12.157052"},{"key":"ref38","first-page":"8","article-title":"ROSIS (Reflective Optics System Imaging Spectrometer)—A candidate instrument for polar platform missions","volume":"868","author":"kunkel","year":"1988","journal-title":"Optoelectronic Technologies for Remote Sensing from Space"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2011.07.024"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2011.11.026"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.13.036503"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2003.08.014"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.3390\/rs11182129","article-title":"Spectral and radiometric calibration of the next generation airborne visible infrared spectrometer (AVIRIS-NG)","volume":"11","author":"chapman","year":"2019","journal-title":"Remote Sens"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/S0094-5765(99)00207-6"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2004.03.012"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2014.02.001"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2014.03.019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2339737"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7050181"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2007.12.014"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2208649"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2637824"},{"key":"ref24","first-page":"b53a","article-title":"Airborne microwave observatory of subcanopy and subsurface (AirMOSS) Earth venture suborbital mission overview","author":"moghaddam","year":"2015","journal-title":"Proc AGUFM"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.901027"},{"key":"ref101","first-page":"1409","article-title":"Sampling based image splitting in large scale distributed computing of Earth observation data","author":"xing","year":"2014","journal-title":"Proc IEEE Geosci Remote Sens Symp"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2014.6946711"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326790"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2009.2037432"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1126\/science.228.4704.1147"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2003.04.007"},{"key":"ref154","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref153","first-page":"1","article-title":"Distributed deep learning with Apache spark and tensorflow","author":"dowling","year":"2018","journal-title":"Proc Eur Spark+AI Summit"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362707"},{"key":"ref155","first-page":"1","article-title":"Hogwild!: A lock-free approach to parallelizing stochastic gradient descent","volume":"24","author":"niu","year":"2011","journal-title":"Proc NIPS"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258338"},{"key":"ref152","first-page":"1","article-title":"Don’t decay the learning rate, increase the batch size","author":"smith","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref151","article-title":"Large batch training of convolutional networks","author":"you","year":"2017","journal-title":"arXiv 1708 03888"},{"key":"ref146","article-title":"Demystifying parallel and distributed deep learning: An in-depth concurrency analysis","author":"ben-nun","year":"2018","journal-title":"arXiv 1802 09941"},{"key":"ref147","first-page":"1305","author":"frank","year":"2005","journal-title":"Weka—A Machine Learning Workbench for Data Mining"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/IC2E.2018.00042"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataCongress.2015.12"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.07.001"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2003.09.007"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.11.021"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3005403"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2952062"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-78548-102-4.50004-1"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2700405"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2010.12.009"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1117\/12.157055","article-title":"HYDICE: An airborne system for hyperspectral imaging","volume":"1937","author":"rickard","year":"1993","journal-title":"Proc SPIE"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2011.940409"},{"key":"ref3","author":"chang","year":"2003","journal-title":"Hyperspectral Imaging Techniques for Spectral Detection and Classification"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2016.2598228"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(98)00064-9"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03200-6"},{"key":"ref8","author":"plaza","year":"2006","journal-title":"High Performance Computing in Remote Sensing"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1029\/2002JE001975"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1201\/9780429488764"},{"key":"ref157","first-page":"583","article-title":"Scaling distributed machine learning with the parameter server","author":"li","year":"2014","journal-title":"Proc Second USENIX Symp Operating Systems Design and Implementation"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/ISDA.2006.95"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MSST.2005.12"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2003.815018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326053"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1002\/9781118945179.ch11"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2008.4779667"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1364\/AO.53.001363"},{"key":"ref41","first-page":"37","article-title":"The HyMap airborne hyperspectral sensor: The system, calibration and performance","author":"cocks","year":"1998","journal-title":"Proc 1st EARSEL Workshop Imag Spectrosc"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2013.6723731"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/rs70708830"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"ref126","first-page":"10","article-title":"MapReduce: Simplified data processing on large clusters","volume":"6","author":"dean","year":"2004","journal-title":"Proc 6th Conf Symp Operating Syst Design Implement"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2015.2404821"},{"key":"ref124","year":"2009","journal-title":"Security Guidance for Critical Areas of Focus in Cloud Computing V2 1"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(02)00172-4"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/JMASS.2020.3019669"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/eScience.2008.59"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2396577"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1145\/1165389.945450"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2012.08.029"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.3390\/rs12081257","article-title":"A new GPU implementation of support vector machines for fast hyperspectral image classification","volume":"12","author":"paoletti","year":"2020","journal-title":"Remote Sens"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1145\/2886107"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2017.10.011"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/SPAC.2017.8304270"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2010.02.008"},{"key":"ref133","first-page":"844","author":"dongarra","year":"2011","journal-title":"HPC Challenge Benchmark"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2011.66"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/CloudCom.2010.107"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2329330"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12636-9_9"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03187-0"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1177\/109434209100500306"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1145\/2287036.2287045"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2010.5461931"},{"key":"ref137","author":"petitet","year":"2018","journal-title":"HPL—A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.04.008"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1145\/1952682.1952705"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2009.5306233"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2934011"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.gsf.2015.07.003"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2843525"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1145\/2885497"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2008.2010275"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2014.2339858"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2011.2139193"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2012.88"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.3390\/rs10020243"},{"key":"ref143","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1145\/567806.567807","article-title":"An updated set of basic linear algebra subprograms (BLAS)","volume":"28","author":"blackford","year":"2002","journal-title":"ACM Trans Math Softw"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-016-1896-3"},{"key":"ref144","article-title":"MPI: A message-passing interface standard","author":"forum","year":"1994"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2729890"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.916090"},{"key":"ref145","article-title":"One weird trick for parallelizing convolutional neural networks","author":"krizhevsky","year":"2014","journal-title":"arXiv 1404 5997"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2948454"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataService.2016.39"},{"key":"ref95","doi-asserted-by":"crossref","first-page":"176","DOI":"10.3390\/rs13020176","article-title":"A parallel unmixing-based content retrieval system for distributed hyperspectral imagery repository on cloud computing platforms","volume":"13","author":"zheng","year":"2021","journal-title":"Remote Sens"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/CCEM.2015.30"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.3390\/rs12010062"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2013.6723126"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.3390\/rs11232881"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/CLEI.2015.7359991"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3026673"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT.2015.7395242"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2929731"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/CCEM.2015.16"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.3390\/rs3061234"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/Geoinformatics.2013.6626187"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CCIS.2012.6664454"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2003.04.002"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/IDAACS.2009.5342987"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2005.10.001"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326066"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2014.7004311"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2012.2194696"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2547020"},{"key":"ref10","first-page":"1","article-title":"The Group on Earth Observations (GEO) through 2025","volume":"40","author":"ryan","year":"2014","journal-title":"Proc 40th COSPAR Sci Assembly"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.800-145"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-007-0048-1"},{"key":"ref13","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2540798"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.04.015"},{"key":"ref118","article-title":"MLlib: Machine learning in Apache spark","author":"meng","year":"2015","journal-title":"arXiv 1505 06807"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2959707"},{"key":"ref117","first-page":"2","article-title":"Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing","author":"zaharia","year":"2012","journal-title":"Proc 9th USENIX Conf Netw Syst Design Implement (NSDI)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.09.006"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2871782"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2010.2095495"},{"key":"ref84","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.3390\/rs11243056","article-title":"Remote sensing big data classification with high performance distributed deep learning","volume":"11","author":"sedona","year":"2019","journal-title":"Remote Sens"},{"key":"ref119","author":"marinescu","year":"2017","journal-title":"Cloud Computing Theory and Practice"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2011.2162643"},{"key":"ref83","article-title":"Democratizing production-scale distributed deep learning","author":"ma","year":"2018","journal-title":"arXiv 1811 00143"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/MSST.2010.5496972"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2012.2205372"},{"key":"ref116","first-page":"10","article-title":"Spark: Cluster computing with working sets","author":"zaharia","year":"2010","journal-title":"Proc 2nd USENIX Conf Hot Topics Cloud Comput (HotCloud)"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2860125"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1145\/2523616.2523633"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1145\/1721654.1721672"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CBD.2015.34"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2005.173"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1145\/2431211.2431216"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2012.03.011"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2020.3016978"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.3390\/rs12172670"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7325813"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2542193"}],"container-title":["Proceedings of the IEEE"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5\/9489264\/09378798.pdf?arnumber=9378798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T17:23:22Z","timestamp":1643217802000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378798\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":159,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/jproc.2021.3063258","relation":{},"ISSN":["0018-9219","1558-2256"],"issn-type":[{"value":"0018-9219","type":"print"},{"value":"1558-2256","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8]]}}}