{"id":"https://openalex.org/W4386523169","doi":"https://doi.org/10.1109/lgrs.2023.3312734","title":"Difference-Enhancement Triplet Network for Change Detection in Multispectral Images","display_name":"Difference-Enhancement Triplet Network for Change Detection in Multispectral Images","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386523169","doi":"https://doi.org/10.1109/lgrs.2023.3312734"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3312734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3312734","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5035190973","display_name":"Wuxia Zhang","orcid":"https://orcid.org/0000-0002-0759-2489"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wuxia Zhang","raw_affiliation_strings":["Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-0759-2489","affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100321494","display_name":"Yuhang Zhang","orcid":"https://orcid.org/0000-0001-5382-6496"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Zhang","raw_affiliation_strings":["Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059826791","display_name":"L.i-Ming Su","orcid":"https://orcid.org/0009-0005-7360-8812"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangxu Su","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083484479","display_name":"Chao Mei","orcid":"https://orcid.org/0000-0002-9212-9746"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210144662","display_name":"Xi'an Institute of Optics and Precision Mechanics","ror":"https://ror.org/0444j5556","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210144662"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Mei","raw_affiliation_strings":["Center for Optical Imagery Analysis and Learning, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Optical Imagery Analysis and Learning, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China","institution_ids":["https://openalex.org/I4210144662","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018824735","display_name":"Xiaoqiang Lu","orcid":"https://orcid.org/0000-0002-7037-5188"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqiang Lu","raw_affiliation_strings":["College of Physics and Information Engineering, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7037-5188","affiliations":[{"raw_affiliation_string":"College of Physics and Information Engineering, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4504,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67480302,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"20","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":0.9997000098228455,"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":0.9997000098228455,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9904999732971191,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9318000078201294,"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/discriminative-model","display_name":"Discriminative model","score":0.8144588470458984},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7647308707237244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7238115072250366},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7154624462127686},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6745944023132324},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.6545445919036865},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.6443033218383789},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5831859707832336},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5159595608711243},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49587282538414},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46631938219070435},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43127351999282837}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8144588470458984},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7647308707237244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7238115072250366},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7154624462127686},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6745944023132324},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.6545445919036865},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.6443033218383789},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5831859707832336},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5159595608711243},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49587282538414},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46631938219070435},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43127351999282837},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3312734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3312734","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2926447122","display_name":null,"funder_award_id":"QYZDB-SSW-JSC015","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"},{"id":"https://openalex.org/G4088419678","display_name":null,"funder_award_id":"XAB2021YN14","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"},{"id":"https://openalex.org/G6329059712","display_name":null,"funder_award_id":"62001378","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7527128939","display_name":null,"funder_award_id":"XIOPMQCH20211012","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2564140372","https://openalex.org/W2884276099","https://openalex.org/W2896365540","https://openalex.org/W2963775347","https://openalex.org/W3027225766","https://openalex.org/W3047443805","https://openalex.org/W3048631361","https://openalex.org/W3099831940","https://openalex.org/W3102692100","https://openalex.org/W3103695279","https://openalex.org/W3171427178","https://openalex.org/W3180045188","https://openalex.org/W4213124617","https://openalex.org/W4226253382","https://openalex.org/W4312373555","https://openalex.org/W4313506322"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W3005969065","https://openalex.org/W2031512949","https://openalex.org/W3119773509","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Change":[0],"detection":[1,17,32,41],"is":[2],"the":[3,21,37,56,68,92,98,104,110,114,122,128,143,171],"process":[4],"of":[5,36,59,131,134,170],"detecting":[6,48],"and":[7,29,51,97,156,174],"evaluating":[8],"differences":[9],"from":[10],"bitemporal":[11],"remote":[12],"sensing":[13],"images.":[14],"Deep-learning-based":[15],"change":[16,31,40,80],"methods":[18,42,177],"have":[19],"become":[20],"mainstream":[22],"approaches":[23],"due":[24],"to":[25,116,138,159],"their":[26],"discriminative":[27,165],"features":[28,60,137],"good":[30],"performance.":[33],"However,":[34],"most":[35],"existing":[38],"deep-learning-based":[39],"did":[43,52],"not":[44,53],"perform":[45],"well":[46],"in":[47],"subtle":[49,140],"changes":[50],"fully":[54],"explore":[55],"underlying":[57,129],"information":[58,130],"learned":[61,135],"by":[62],"deep":[63,75],"neural":[64,76],"networks.":[65],"To":[66],"address":[67],"above-mentioned":[69],"problems,":[70],"we":[71],"propose":[72],"an":[73],"end-to-end":[74],"network":[77,85],"for":[78],"multispectral":[79],"detection,":[81],"named":[82],"difference-enhancement":[83],"triplet":[84,93,105,152],"(DETNet).":[86],"DETNet":[87,161,173],"mainly":[88],"includes":[89,151],"two":[90],"modules:":[91],"feature":[94,100,106,124],"extraction":[95,107],"module":[96,108,126],"difference":[99,123,132],"learning":[101,125,163],"module.":[102],"First,":[103],"uses":[109,145],"triple":[111],"CNN":[112],"as":[113],"backbone":[115],"extract":[117],"representative":[118],"spatial\u2013spectral":[119,136],"features.":[120,166],"Second,":[121],"mines":[127],"representations":[133],"detect":[139],"changes.":[141],"Finally,":[142],"model":[144],"a":[146],"compound":[147],"loss":[148],"function,":[149],"which":[150],"loss,":[153,155,158],"contrastive":[154],"cross-entropy":[157],"guide":[160],"toward":[162],"more":[164],"Extensive":[167],"experimental":[168],"results":[169],"proposed":[172],"other":[175],"state-of-the-art":[176],"on":[178],"four":[179],"datasets":[180],"demonstrate":[181],"its":[182],"superiority.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}