{"id":"https://openalex.org/W4391093832","doi":"https://doi.org/10.1109/bigdata59044.2023.10386306","title":"DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data","display_name":"DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391093832","doi":"https://doi.org/10.1109/bigdata59044.2023.10386306"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101892149","display_name":"Xin Huang","orcid":"https://orcid.org/0000-0002-5909-5935"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xin Huang","raw_affiliation_strings":["Towson University,Department of Computer and Information Sciences,Baltimore,MD,USA","Department of Computer and Information Sciences, Towson University, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"Towson University,Department of Computer and Information Sciences,Baltimore,MD,USA","institution_ids":["https://openalex.org/I4322298"]},{"raw_affiliation_string":"Department of Computer and Information Sciences, Towson University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100334376","display_name":"Chenxi Wang","orcid":"https://orcid.org/0000-0003-0550-8025"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenxi Wang","raw_affiliation_strings":["University of Maryland, Baltimore County,Goddard Earth Sciences Technology and Research (GESTAR) II,Baltimore,MD,USA","Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County,Goddard Earth Sciences Technology and Research (GESTAR) II,Baltimore,MD,USA","institution_ids":["https://openalex.org/I79272384"]},{"raw_affiliation_string":"Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100710814","display_name":"Wenbin Zhang","orcid":"https://orcid.org/0000-0003-3024-5415"},"institutions":[{"id":"https://openalex.org/I11957088","display_name":"Michigan Technological University","ror":"https://ror.org/0036rpn28","country_code":"US","type":"education","lineage":["https://openalex.org/I11957088"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenbin Zhang","raw_affiliation_strings":["Michigan Technology University,Department of Computer Science,Houghton,MI,USA","Department of Computer Science, Michigan Technology University, Houghton, MI, USA"],"affiliations":[{"raw_affiliation_string":"Michigan Technology University,Department of Computer Science,Houghton,MI,USA","institution_ids":["https://openalex.org/I11957088"]},{"raw_affiliation_string":"Department of Computer Science, Michigan Technology University, Houghton, MI, USA","institution_ids":["https://openalex.org/I11957088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017846156","display_name":"Sanjay Purushotham","orcid":"https://orcid.org/0000-0003-4315-7916"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjay Purushotham","raw_affiliation_strings":["University of Maryland, Baltimore County,Department of Information Systems,Baltimore,MD,USA","Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County,Department of Information Systems,Baltimore,MD,USA","institution_ids":["https://openalex.org/I79272384"]},{"raw_affiliation_string":"Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101750217","display_name":"Jianwu Wang","orcid":"https://orcid.org/0000-0002-9933-1170"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianwu Wang","raw_affiliation_strings":["University of Maryland, Baltimore County,Department of Information Systems,Baltimore,MD,USA","Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County,Department of Information Systems,Baltimore,MD,USA","institution_ids":["https://openalex.org/I79272384"]},{"raw_affiliation_string":"Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101892149"],"corresponding_institution_ids":["https://openalex.org/I4322298"],"apc_list":null,"apc_paid":null,"fwci":0.325,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64709829,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1410","last_page":"1418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.736007571220398},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7130928039550781},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6372878551483154},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.6166932582855225},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5714120864868164},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.551604151725769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47601252794265747},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46949082612991333},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1354321539402008},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1097155213356018},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08963412046432495},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07450100779533386},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.06882324814796448}],"concepts":[{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.736007571220398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7130928039550781},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6372878551483154},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.6166932582855225},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5714120864868164},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.551604151725769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47601252794265747},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46949082612991333},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1354321539402008},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1097155213356018},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08963412046432495},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07450100779533386},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.06882324814796448},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1490960179","https://openalex.org/W1606767976","https://openalex.org/W1731081199","https://openalex.org/W2018564725","https://openalex.org/W2030038151","https://openalex.org/W2035570184","https://openalex.org/W2051481444","https://openalex.org/W2103977502","https://openalex.org/W2104068492","https://openalex.org/W2149466042","https://openalex.org/W2164943005","https://openalex.org/W2168158289","https://openalex.org/W2194775991","https://openalex.org/W2214409633","https://openalex.org/W2336774813","https://openalex.org/W2412588858","https://openalex.org/W2540743583","https://openalex.org/W2888084946","https://openalex.org/W2905857158","https://openalex.org/W2919115771","https://openalex.org/W2964288524","https://openalex.org/W2995280114","https://openalex.org/W3021632667","https://openalex.org/W3139054171","https://openalex.org/W4294170691","https://openalex.org/W4309652652","https://openalex.org/W6637618735","https://openalex.org/W6639113993","https://openalex.org/W6682691769","https://openalex.org/W6684149856","https://openalex.org/W6743095615","https://openalex.org/W6802516332"],"related_works":["https://openalex.org/W2789518417","https://openalex.org/W4213217485","https://openalex.org/W2566545183","https://openalex.org/W2499449816","https://openalex.org/W2059085722","https://openalex.org/W4210856988","https://openalex.org/W4375867731","https://openalex.org/W238145802","https://openalex.org/W2946721565","https://openalex.org/W2801976440"],"abstract_inverted_index":{"Collocation":[0],"of":[1,13,64,85,109,120,133,136,221],"measurements":[2],"from":[3,15,50],"active":[4,52,66],"and":[5,34,91,153,166,199,231],"passive":[6,215],"satellite":[7],"sensors":[8,17],"refers":[9],"to":[10,81,157,185,194,209],"the":[11,20,26,51,57,62,82,89,106,117,131,164,195,219],"combination":[12],"data":[14,94,111,122,139,177],"two":[16],"that":[18,102,190],"observe":[19],"same":[21,27],"geographic":[22],"area":[23],"at":[24],"nearly":[25],"time":[28],"but":[29,54],"with":[30,46,113,151,178,218],"differing":[31],"spatial":[32,73],"resolutions":[33],"viewing":[35],"angles.":[36],"This":[37],"collocated":[38],"data,":[39,44,184,217],"often":[40],"known":[41],"as":[42],"on-track":[43,114,165],"comes":[45],"precise":[47],"product":[48],"labels":[49],"sensor":[53],"comprises":[55],"only":[56],"pixels":[58],"located":[59],"directly":[60],"on":[61,206],"path":[63],"an":[65],"satellite\u2019s":[67],"orbit.":[68],"As":[69],"a":[70,146,159,187],"result,":[71],"its":[72],"coverage":[74],"is":[75,95,172,204],"quite":[76],"limited,":[77],"especially":[78],"when":[79],"compared":[80],"vast":[83],"quantities":[84],"off-track":[86,93,121,138,167,176,183,245],"data.":[87,115,168,248],"Handling":[88],"abundant":[90],"information-dense":[92],"crucial":[96],"for":[97,126,163,244],"training":[98],"machine":[99],"learning":[100,149],"models":[101],"can":[103,191],"effectively":[104],"integrate":[105],"unique":[107],"features":[108],"this":[110],"along":[112],"However,":[116],"sheer":[118],"volume":[119],"presents":[123],"significant":[124],"challenges":[125,132],"these":[127,207],"models.":[128],"To":[129],"address":[130],"large":[134],"amounts":[135],"unlabeled":[137],"in":[140,214,225,240],"remote":[141,246],"sensing":[142,216,247],"applications,":[143],"we":[144],"introduce":[145],"self-supervised":[147],"representation":[148,189],"model":[150,181],"VAE":[152,179],"domain":[154,160,197],"adaptation":[155,198],"methods":[156],"learn":[158,186],"invariant":[161],"classifier":[162,203],"The":[169,202],"model\u2019s":[170],"performance":[171],"enhanced":[173],"by":[174],"pre-training":[175],"generative":[180],"using":[182],"good":[188],"be":[192],"transferred":[193],"down-streaming":[196],"classification":[200],"tasks.":[201],"built":[205],"representations":[208],"classify":[210],"different":[211],"cloud":[212,226,241],"types":[213],"goal":[220],"achieving":[222],"higher":[223,238],"accuracy":[224,239],"property":[227,242],"retrieval.":[228],"Extensive":[229],"quantitative":[230],"qualitative":[232],"evaluation":[233],"demonstrate":[234],"our":[235],"method":[236],"achieves":[237],"retrieval":[243]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
