{"id":"https://openalex.org/W2992395948","doi":"https://doi.org/10.1109/whispers.2019.8921180","title":"Land Cover Classification for Satellite Images Through 1D CNN","display_name":"Land Cover Classification for Satellite Images Through 1D CNN","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2992395948","doi":"https://doi.org/10.1109/whispers.2019.8921180","mag":"2992395948"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2019.8921180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2019.8921180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","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/A5041567418","display_name":"Yang Song","orcid":"https://orcid.org/0000-0003-1283-1672"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yang Song","raw_affiliation_strings":["Department of EECS, The University of Tennessee, Knoxville, TN, US"],"affiliations":[{"raw_affiliation_string":"Department of EECS, The University of Tennessee, Knoxville, TN, US","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100626848","display_name":"Zhifei Zhang","orcid":"https://orcid.org/0000-0002-3575-7826"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhifei Zhang","raw_affiliation_strings":["Department of EECS, The University of Tennessee, Knoxville, TN, US"],"affiliations":[{"raw_affiliation_string":"Department of EECS, The University of Tennessee, Knoxville, TN, US","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037807767","display_name":"Razieh Kaviani Baghbaderani","orcid":null},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Razieh Kaviani Baghbaderani","raw_affiliation_strings":["Department of EECS, The University of Tennessee, Knoxville, TN, US"],"affiliations":[{"raw_affiliation_string":"Department of EECS, The University of Tennessee, Knoxville, TN, US","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058742643","display_name":"Fanqi Wang","orcid":"https://orcid.org/0000-0002-0933-984X"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fanqi Wang","raw_affiliation_strings":["Department of EECS, The University of Tennessee, Knoxville, TN, US"],"affiliations":[{"raw_affiliation_string":"Department of EECS, The University of Tennessee, Knoxville, TN, US","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082228369","display_name":"Ying Qu","orcid":"https://orcid.org/0000-0002-4613-8625"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Qu","raw_affiliation_strings":["Department of EECS, The University of Tennessee, Knoxville, TN, US"],"affiliations":[{"raw_affiliation_string":"Department of EECS, The University of Tennessee, Knoxville, TN, US","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056264752","display_name":"Craig Stuttsy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144946","display_name":"Applied Research Associates (United States)","ror":"https://ror.org/04vpnmr86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210144946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Craig Stuttsy","raw_affiliation_strings":["Southeast Division, Applied Research Associates, Raleigh, NC, US"],"affiliations":[{"raw_affiliation_string":"Southeast Division, Applied Research Associates, Raleigh, NC, US","institution_ids":["https://openalex.org/I4210144946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072730926","display_name":"Hairong Qi","orcid":"https://orcid.org/0000-0002-2693-5520"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hairong Qi","raw_affiliation_strings":["Department of EECS, The University of Tennessee, Knoxville, TN, US"],"affiliations":[{"raw_affiliation_string":"Department of EECS, The University of Tennessee, Knoxville, TN, US","institution_ids":["https://openalex.org/I75027704"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5041567418"],"corresponding_institution_ids":["https://openalex.org/I75027704"],"apc_list":null,"apc_paid":null,"fwci":1.6317,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.8662576,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.73884117603302},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.680640459060669},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6553252339363098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6525605916976929},{"id":"https://openalex.org/keywords/classification-of-discontinuities","display_name":"Classification of discontinuities","score":0.5994012951850891},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5420724749565125},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5329759120941162},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.5287192463874817},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5278013944625854},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.50334233045578},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5017964839935303},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49782490730285645},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.47328847646713257},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.44109973311424255},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4327428936958313},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42913568019866943},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4166756570339203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2922018766403198},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2641127109527588},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.1946200132369995},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14761587977409363},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0918608009815216}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73884117603302},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.680640459060669},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6553252339363098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6525605916976929},{"id":"https://openalex.org/C15627037","wikidata":"https://www.wikidata.org/wiki/Q541961","display_name":"Classification of discontinuities","level":2,"score":0.5994012951850891},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5420724749565125},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5329759120941162},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5287192463874817},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5278013944625854},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.50334233045578},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5017964839935303},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49782490730285645},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.47328847646713257},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.44109973311424255},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4327428936958313},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42913568019866943},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4166756570339203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2922018766403198},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2641127109527588},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.1946200132369995},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14761587977409363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0918608009815216},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2019.8921180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2019.8921180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1976615758","https://openalex.org/W1985864794","https://openalex.org/W2050830693","https://openalex.org/W2101837437","https://openalex.org/W2127062304","https://openalex.org/W2160558851","https://openalex.org/W2267317359","https://openalex.org/W2291961022","https://openalex.org/W2484692031","https://openalex.org/W2604086375","https://openalex.org/W2611200606","https://openalex.org/W2776964412","https://openalex.org/W2794833149","https://openalex.org/W2798551784","https://openalex.org/W2807536200","https://openalex.org/W2964121744","https://openalex.org/W2964194231","https://openalex.org/W4300176521","https://openalex.org/W6631190155","https://openalex.org/W6744073472"],"related_works":["https://openalex.org/W1972096828","https://openalex.org/W2529137940","https://openalex.org/W4302048708","https://openalex.org/W2359913921","https://openalex.org/W1595194509","https://openalex.org/W4239740410","https://openalex.org/W1996195943","https://openalex.org/W2020734820","https://openalex.org/W2043913960","https://openalex.org/W3129683637"],"abstract_inverted_index":{"Land":[0,21],"cover":[1,22],"classification":[2,23,122],"of":[3,49,137],"satellite":[4,68],"imagery":[5],"can":[6],"provide":[7],"significant":[8],"information":[9],"for":[10,115],"many":[11],"applications,":[12],"including":[13],"surface":[14],"analysis,":[15],"environmental":[16],"monitoring,":[17],"building":[18],"reconstruction,":[19],"etc.":[20],"has":[24],"been":[25],"generally":[26],"performed":[27],"using":[28,133],"unmixing-based":[29,37,128],"or":[30],"shallow/deep":[31],"learning":[32],"approaches,":[33],"among":[34],"which":[35,63],"the":[36,45,50,106],"approaches":[38,54,87],"suffer":[39],"from":[40],"stability":[41],"issues":[42],"due":[43],"to":[44,76,102],"complex":[46],"intrinsic":[47],"properties":[48],"data,":[51],"deep":[52],"learning-based":[53],"like":[55],"2D":[56,85],"CNN":[57,86],"requires":[58],"large":[59],"labeled":[60],"training":[61,138],"set":[62],"is":[64],"often":[65],"unavailable":[66],"in":[67,81,105],"images":[69],"and":[70,129],"small":[71],"ground":[72],"truth":[73],"collection":[74],"leads":[75],"spatial":[77],"discontinuities":[78],"(as":[79],"shown":[80],"Fig.":[82],"1),":[83],"making":[84],"unviable.":[88],"In":[89],"this":[90],"paper,":[91],"we":[92,110],"first":[93],"propose":[94],"a":[95],"1D":[96],"convolution":[97],"neural":[98,130],"network-based":[99],"framework":[100],"applied":[101],"each":[103],"pixel":[104],"spectral":[107],"domain":[108],"where":[109],"extract":[111],"descriptive":[112],"local":[113],"features":[114],"improved":[116],"classification.":[117],"Experimental":[118],"results":[119],"demonstrate":[120],"superior":[121],"accuracy":[123],"through":[124],"comparison":[125],"with":[126],"traditional":[127],"network":[131],"methods":[132],"just":[134],"limited":[135],"number":[136],"samples.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}