{"id":"https://openalex.org/W3152716313","doi":"https://doi.org/10.1109/jstars.2021.3073719","title":"EMFNet: Enhanced Multisource Fusion Network for Land Cover Classification","display_name":"EMFNet: Enhanced Multisource Fusion Network for Land Cover Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3152716313","doi":"https://doi.org/10.1109/jstars.2021.3073719","mag":"3152716313"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2021.3073719","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3073719","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/jstars.2021.3073719","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100667413","display_name":"Chengxiang Li","orcid":"https://orcid.org/0000-0002-2490-5539"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengxiang Li","raw_affiliation_strings":["Jiangsu Key Laboratory of Big Data Analysis Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Big Data Analysis Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086507196","display_name":"Renlong Hang","orcid":"https://orcid.org/0000-0001-6046-3689"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renlong Hang","raw_affiliation_strings":["Jiangsu Key Laboratory of Big Data Analysis Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6046-3689","affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Big Data Analysis Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042657950","display_name":"Behnood Rasti","orcid":"https://orcid.org/0000-0002-1091-9841"},"institutions":[{"id":"https://openalex.org/I2801798921","display_name":"Helmholtz-Zentrum Dresden-Rossendorf","ror":"https://ror.org/01zy2cs03","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921"]},{"id":"https://openalex.org/I4210148560","display_name":"Helmholtz Institute Freiberg for Resource Technology","ror":"https://ror.org/04kdb0j04","country_code":"DE","type":"government","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921","https://openalex.org/I4210148560"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Behnood Rasti","raw_affiliation_strings":["Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany"],"raw_orcid":"https://orcid.org/0000-0002-1091-9841","affiliations":[{"raw_affiliation_string":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany","institution_ids":["https://openalex.org/I2801798921","https://openalex.org/I4210148560"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1800,"currency":"USD","value_usd":1800},"apc_paid":{"value":1800,"currency":"USD","value_usd":1800},"fwci":2.494,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.89442939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"14","issue":null,"first_page":"4381","last_page":"4389"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9858999848365784,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7909491062164307},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.7710026502609253},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6932199001312256},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6287825703620911},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6208533048629761},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5859516859054565},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5816437602043152},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5494338274002075},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46455520391464233},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4410816431045532},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4316618740558624},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39940619468688965},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.335465669631958},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.071796715259552}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7909491062164307},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.7710026502609253},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6932199001312256},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6287825703620911},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6208533048629761},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5859516859054565},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5816437602043152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5494338274002075},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46455520391464233},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4410816431045532},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4316618740558624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39940619468688965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.335465669631958},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.071796715259552},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2021.3073719","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3073719","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2bd69a56b95e41638aad33e2662876ca","is_oa":true,"landing_page_url":"https://doaj.org/article/2bd69a56b95e41638aad33e2662876ca","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4381-4389 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2021.3073719","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3073719","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1183509794","display_name":null,"funder_award_id":"61906096,61802199","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7833602292","display_name":null,"funder_award_id":"BK20180786","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1497089125","https://openalex.org/W1965309615","https://openalex.org/W1976416886","https://openalex.org/W2029992428","https://openalex.org/W2054689043","https://openalex.org/W2296450878","https://openalex.org/W2346557146","https://openalex.org/W2565258258","https://openalex.org/W2599078391","https://openalex.org/W2602024454","https://openalex.org/W2606929568","https://openalex.org/W2609880332","https://openalex.org/W2623518586","https://openalex.org/W2744049245","https://openalex.org/W2746661731","https://openalex.org/W2765739551","https://openalex.org/W2899869235","https://openalex.org/W2923136550","https://openalex.org/W2940726923","https://openalex.org/W2945608588","https://openalex.org/W2947295162","https://openalex.org/W2969614292","https://openalex.org/W3000547655","https://openalex.org/W3004968762","https://openalex.org/W3008439211","https://openalex.org/W3035356612","https://openalex.org/W3037458146","https://openalex.org/W3042772844","https://openalex.org/W3046027728","https://openalex.org/W3047443805","https://openalex.org/W3048631361","https://openalex.org/W3080181119","https://openalex.org/W3081753142","https://openalex.org/W3101640299","https://openalex.org/W3102692100","https://openalex.org/W3103695279","https://openalex.org/W3105997607","https://openalex.org/W3122774149","https://openalex.org/W6657934682"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W1971268144","https://openalex.org/W4390606538"],"abstract_inverted_index":{"Feature":[0],"extraction":[1],"and":[2,135],"fusion":[3,22,97,106,129],"are":[4,37,62],"two":[5,33,43,84,140],"critical":[6],"issues":[7],"for":[8,101],"the":[9,66,76,81,118,132,136,147],"task":[10],"of":[11,51,69,75,83,120],"multisource":[12,21],"classification.":[13],"In":[14,114],"this":[15,112],"article,":[16],"we":[17,124],"propose":[18],"an":[19,29],"enhanced":[20],"network":[23,47],"(EMFNet)":[24],"to":[25,39,64,92,110,116],"address":[26],"them":[27],"in":[28,154],"end-to-end":[30],"framework.":[31],"Specifically,":[32],"convolutional":[34,53,57],"neural":[35],"networks":[36],"employed":[38],"extract":[40],"features":[41,82],"from":[42],"different":[44,102],"sources.":[45],"Each":[46],"is":[48,90,108],"mainly":[49],"comprised":[50],"three":[52],"layers.":[54],"For":[55],"each":[56],"layer,":[58],"feature":[59,68,105],"tuning":[60],"modules":[61],"designed":[63,109],"enhance":[65],"extracted":[67],"one":[70],"source":[71],"by":[72],"taking":[73],"advantage":[74],"other":[77],"source.":[78],"After":[79],"getting":[80],"sources,":[85],"a":[86,104],"weighted":[87],"summation":[88],"method":[89],"used":[91],"fuse":[93],"them.":[94,157],"Considering":[95],"that":[96,146],"weights":[98],"should":[99],"vary":[100],"inputs,":[103],"module":[107],"achieve":[111,150],"goal.":[113],"order":[115],"test":[117],"performance":[119],"our":[121],"proposed":[122],"EMFNet,":[123],"compare":[125],"it":[126],"with":[127,156],"state-of-the-art":[128],"models,":[130,138],"including":[131],"traditional":[133],"models":[134],"deep-learning-based":[137],"on":[139],"real":[141],"datasets.":[142],"Experimental":[143],"results":[144,153],"show":[145],"EMFNet":[148],"can":[149],"competitive":[151],"classification":[152],"comparison":[155]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}