{"id":"https://openalex.org/W4387803807","doi":"https://doi.org/10.1109/igarss52108.2023.10281537","title":"Performance Comparision of VGG-16 and ResNet-34 Algorithms for Supervised Classification of Landsat Images","display_name":"Performance Comparision of VGG-16 and ResNet-34 Algorithms for Supervised Classification of Landsat Images","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387803807","doi":"https://doi.org/10.1109/igarss52108.2023.10281537"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10281537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","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/A5031047092","display_name":"Varsha Bhosale","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Varsha Bhosale","raw_affiliation_strings":["Vidyalankar Institute of Technology,Mumbai,Maharashtra,India","Vidyalankar Institute of Technology, Mumbai, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Vidyalankar Institute of Technology,Mumbai,Maharashtra,India","institution_ids":[]},{"raw_affiliation_string":"Vidyalankar Institute of Technology, Mumbai, Maharashtra, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048066977","display_name":"Archana B. Patankar","orcid":"https://orcid.org/0009-0001-2305-3776"},"institutions":[{"id":"https://openalex.org/I899992584","display_name":"Piramal (India)","ror":"https://ror.org/00vj60w13","country_code":"IN","type":"company","lineage":["https://openalex.org/I899992584"]},{"id":"https://openalex.org/I212738717","display_name":"Dwarkadas J. Sanghvi College of Engineering","ror":"https://ror.org/04d4hxn32","country_code":"IN","type":"education","lineage":["https://openalex.org/I212738717"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Archana Patankar","raw_affiliation_strings":["Thadomal Sahani College of Engineering,Mumbai,Maharashtra,India","Thadomal Sahani College of Engineering, Mumbai, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Thadomal Sahani College of Engineering,Mumbai,Maharashtra,India","institution_ids":["https://openalex.org/I899992584"]},{"raw_affiliation_string":"Thadomal Sahani College of Engineering, Mumbai, Maharashtra, India","institution_ids":["https://openalex.org/I212738717"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031047092"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.325,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62848038,"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":"6247","last_page":"6250"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9941999912261963,"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":0.9908999800682068,"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/computer-science","display_name":"Computer science","score":0.7085486650466919},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6946520805358887},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5864238739013672},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.577448844909668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5338303446769714},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.5171769857406616},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5128265023231506},{"id":"https://openalex.org/keywords/thematic-mapper","display_name":"Thematic Mapper","score":0.5063251256942749},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.48086076974868774},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47304481267929077},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.4384942650794983},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41471225023269653},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4134794771671295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40490609407424927},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.2658247947692871},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21742060780525208},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1851418912410736},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1811719536781311}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7085486650466919},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6946520805358887},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5864238739013672},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.577448844909668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5338303446769714},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.5171769857406616},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5128265023231506},{"id":"https://openalex.org/C2775938548","wikidata":"https://www.wikidata.org/wiki/Q1565182","display_name":"Thematic Mapper","level":3,"score":0.5063251256942749},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.48086076974868774},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47304481267929077},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.4384942650794983},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41471225023269653},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4134794771671295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40490609407424927},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.2658247947692871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21742060780525208},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1851418912410736},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1811719536781311},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10281537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2194775991","https://openalex.org/W2610166850","https://openalex.org/W2985778816","https://openalex.org/W4298619687","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W2063262193","https://openalex.org/W2342509369","https://openalex.org/W2061775097","https://openalex.org/W4318664220","https://openalex.org/W2960762821","https://openalex.org/W769648878","https://openalex.org/W2079278374","https://openalex.org/W2471096927","https://openalex.org/W2006362812","https://openalex.org/W1594962745"],"abstract_inverted_index":{"The":[0,179,190],"rapid":[1],"change":[2],"in":[3],"urban":[4,28],"landscape":[5,11],"across":[6],"the":[7,38,91,94,114,125,135,145,166,169,202],"globe":[8],"demands":[9],"accurate":[10],"feature":[12,46],"extraction":[13,47],"techniques.":[14],"Remote":[15],"sensing":[16],"images":[17,41,124],"obtain":[18],"terrain":[19],"information":[20],"which":[21,138],"can":[22],"be":[23],"used":[24],"to":[25,36,44,58,83,89,161],"extract":[26],"various":[27,191],"features":[29],"of":[30,64,93],"land":[31,34],"use":[32],"and":[33,48,52,99,120,149,157,172,184,195,206],"cover":[35],"identify":[37],"changes.":[39],"These":[40],"enable":[42],"us":[43],"perform":[45,59,162],"classification.":[49],"Several":[50],"powerful":[51],"successful":[53],"mechanisms":[54],"have":[55,78],"been":[56],"proposed":[57],"supervised":[60,71,111,131],"satellite":[61,67,72,123],"image":[62,73,132],"categorization":[63],"remotely":[65,115],"sensed":[66,116],"multispectral":[68,117],"data.":[69],"In":[70],"classification,":[74],"machine":[75],"learning":[76],"algorithms":[77,101],"become":[79],"quite":[80],"popular":[81],"due":[82],"their":[84],"good":[85],"accuracy.This":[86],"paper":[87],"aims":[88],"study":[90,136],"performance":[92],"Convolution":[95],"Neural":[96],"Network's":[97],"VGG-16":[98],"ResNet-34":[100],"for":[102,144,201],"identifying":[103],"Land":[104,106],"Use":[105],"Cover":[107],"Change":[108],"(LULCC)":[109],"using":[110],"classification.Based":[112],"on":[113,134],"Landsat":[118,121],"5":[119],"8":[122],"LULCC":[126,192],"is":[127,139],"found":[128],"by":[129],"performing":[130],"classification":[133],"region":[137],"Mumbai":[140],"Metropolitan":[141],"Region":[142],"(MMR)":[143],"years":[146],"2000,":[147,204],"2010":[148,205],"2020.":[150,207],"Four":[151],"classes":[152],"as":[153],"Water,":[154],"Built-up,":[155],"Vegetation":[156],"Barrenland":[158],"were":[159,176,187,199],"identified":[160],"classification.The":[163],"overall":[164],"accuracy,":[165,168,171],"producer's":[167],"user's":[170],"standard":[173],"kappa":[174],"coefficient":[175],"also":[177,188],"calculated.":[178],"training":[180,185],"loss,":[181],"validation":[182],"loss":[183],"accuracy":[186,196],"studied.":[189],"thematic":[193],"maps":[194],"assessment":[197],"results":[198],"obtained":[200],"year":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
