{"id":"https://openalex.org/W3205465097","doi":"https://doi.org/10.1109/igarss47720.2021.9554051","title":"Semantic Segmentation of Land Use / Land Cover (LU/LC) Types Using F-CNNS on Multi-Sensor (Radar-Ir-Optical) Image Data","display_name":"Semantic Segmentation of Land Use / Land Cover (LU/LC) Types Using F-CNNS on Multi-Sensor (Radar-Ir-Optical) Image Data","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205465097","doi":"https://doi.org/10.1109/igarss47720.2021.9554051","mag":"3205465097"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554051","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5091198094","display_name":"Usman Ahmed","orcid":"https://orcid.org/0000-0002-3933-4273"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Usman Iqbal Ahmed","raw_affiliation_strings":["SARlab, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SARlab, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077984987","display_name":"Arturo Velasco","orcid":"https://orcid.org/0009-0002-1904-0830"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arturo Velasco","raw_affiliation_strings":["SARlab, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SARlab, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077333590","display_name":"Bernhard Rabus","orcid":"https://orcid.org/0000-0002-3300-7423"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Bernhard Rabus","raw_affiliation_strings":["SARlab, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SARlab, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I18014758"],"apc_list":null,"apc_paid":null,"fwci":0.1266,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.50320171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4700","last_page":"4703"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"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":0.9991999864578247,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11609","display_name":"Geophysical Methods and Applications","score":0.9936000108718872,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6843268275260925},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6492040157318115},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6377138495445251},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6219058036804199},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5850685238838196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5839046835899353},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.5715998411178589},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5455933213233948},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5032767653465271},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4263726770877838},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42381128668785095},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37371575832366943},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3509599566459656},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18751421570777893},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.17092233896255493}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6843268275260925},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6492040157318115},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6377138495445251},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6219058036804199},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5850685238838196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5839046835899353},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.5715998411178589},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5455933213233948},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5032767653465271},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4263726770877838},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42381128668785095},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37371575832366943},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3509599566459656},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18751421570777893},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.17092233896255493},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.0},{"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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554051","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W357677209","https://openalex.org/W1901129140","https://openalex.org/W1910657905","https://openalex.org/W2764034829","https://openalex.org/W2774688698","https://openalex.org/W2782522152","https://openalex.org/W2963881378","https://openalex.org/W2963995737","https://openalex.org/W2981227223","https://openalex.org/W2999000103","https://openalex.org/W3035665735","https://openalex.org/W3047317383","https://openalex.org/W3088064622","https://openalex.org/W6612074021"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W4392566681","https://openalex.org/W2021379394","https://openalex.org/W2008518276","https://openalex.org/W2152662390","https://openalex.org/W2615806692","https://openalex.org/W2080965330","https://openalex.org/W2547534968"],"abstract_inverted_index":{"Land":[0,2],"Use/":[1],"Cover":[3],"(LU/LC)":[4],"segmentation":[5,64,77,130],"is":[6],"a":[7,207],"widely":[8],"studied":[9,74,144],"topic":[10],"in":[11],"the":[12,29,47,141,152,214,228],"field":[13],"of":[14,50,129,131,148,202,216,230],"remote":[15],"sensing.":[16],"Past":[17],"focus":[18],"has":[19],"been":[20,72],"on":[21,25,35],"independent":[22],"studies":[23],"either":[24],"color":[26],"(RGB)":[27],"and":[28,56,81,102,115,151,168,194,218],"Normalized":[30],"Vegetation":[31],"Index":[32],"(NDVI)":[33],"or":[34],"Polarimetric":[36],"Synthetic":[37],"Aperture":[38],"Radar":[39],"(PolSAR)":[40],"data.":[41],"In":[42],"this":[43,96,175],"paper":[44],"we":[45],"explore":[46],"fusion":[48],"potential":[49,128],"RGB":[51,222],"images":[52,60,104],"with":[53,79],"additional":[54],"SAR":[55,101,217],"Near":[57],"Infra-red":[58],"(NIR)":[59],"for":[61,75,95,125,159,174,206],"enhanced":[62],"LU/LC":[63,231],"through":[65,107],"Fully-Convolutional":[66],"Neural":[67],"Networks":[68],"(F-CNNs).":[69],"F-CNNs":[70],"have":[71],"extensively":[73],"semantic":[76],"problems":[78],"U-Net":[80],"SegNet":[82],"being":[83],"two":[84],"well-known":[85],"F-CNN":[86],"architectures.":[87,161],"Both":[88],"these":[89,149],"architectures":[90],"were":[91,105,143,154],"used":[92],"as":[93,137],"references":[94],"study.":[97],"High":[98],"resolution":[99],"RGB,":[100],"NIR":[103],"acquired":[106],"Google":[108],"Earth":[109],"(GE),":[110],"German":[111],"Aerospace":[112],"Center":[113],"(DLR)":[114],"The":[116,182],"Planet":[117],"Laboratories,":[118],"respectively.":[119],"IR":[120,219],"was":[121,172,184,204],"converted":[122],"to":[123,140,156,221],"NDVI":[124],"its":[126,169,178],"higher":[127],"vegetations":[132],"areas.":[133],"Four":[134],"multi-sensor":[135],"configurations":[136],"input":[138],"channels":[139,158,220],"networks":[142],"after":[145],"precise":[146],"co-registration":[147],"images,":[150],"results":[153],"compared":[155],"individual":[157],"both":[160],"Simon":[162],"Fraser":[163],"University":[164],"(SFU),":[165],"Burnaby":[166],"Campus":[167],"surrounding":[170],"area":[171,183],"selected":[173],"study":[176],"due":[177],"diverse":[179],"land":[180],"types.":[181],"divided":[185],"into":[186],"5":[187],"classes":[188],"i.e.":[189],"Roads,":[190],"Buildings,":[191],"Forest,":[192],"Water":[193],"No":[195],"class":[196],"(unclassified).":[197],"An":[198],"overall,":[199],"best":[200],"accuracy":[201],"~86%":[203],"achieved":[205],"five-channel":[208],"configuration":[209],"(R+G+B+SAR+NDVI).":[210],"We":[211],"show":[212],"that":[213],"inclusion":[215],"based":[223],"network":[224],"can":[225],"significantly":[226],"improve":[227],"performance":[229],"segmentation.":[232]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}