{"id":"https://openalex.org/W4382137977","doi":"https://doi.org/10.3390/rs15133241","title":"Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data","display_name":"Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data","publication_year":2023,"publication_date":"2023-06-23","ids":{"openalex":"https://openalex.org/W4382137977","doi":"https://doi.org/10.3390/rs15133241"},"language":"en","primary_location":{"id":"doi:10.3390/rs15133241","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15133241","pdf_url":"https://www.mdpi.com/2072-4292/15/13/3241/pdf?version=1687680014","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/13/3241/pdf?version=1687680014","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084562901","display_name":"Prathiba A. Palanisamy","orcid":"https://orcid.org/0000-0002-0717-8739"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Prathiba A. Palanisamy","raw_affiliation_strings":["Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041706388","display_name":"Kamal Jain","orcid":"https://orcid.org/0000-0001-5153-0405"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kamal Jain","raw_affiliation_strings":["Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India"],"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035126116","display_name":"Stefania Bonafoni","orcid":"https://orcid.org/0000-0002-6485-393X"},"institutions":[{"id":"https://openalex.org/I27483092","display_name":"University of Perugia","ror":"https://ror.org/00x27da85","country_code":"IT","type":"education","lineage":["https://openalex.org/I27483092"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Stefania Bonafoni","raw_affiliation_strings":["Department of Engineering, University of Perugia, 06125 Perugia, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Engineering, University of Perugia, 06125 Perugia, Italy","institution_ids":["https://openalex.org/I27483092"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084562901"],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":4.763,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.95548305,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"15","issue":"13","first_page":"3241","last_page":"3241"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9932000041007996,"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/multispectral-image","display_name":"Multispectral image","score":0.7350014448165894},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6529363393783569},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6352683901786804},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6124045252799988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6049957275390625},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5515475273132324},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5480790138244629},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5389917492866516},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5239858031272888},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4691770374774933},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4645061194896698},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.44194793701171875},{"id":"https://openalex.org/keywords/spectral-bands","display_name":"Spectral bands","score":0.4249448776245117},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.41693830490112305},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.1596372425556183},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.11318546533584595}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7350014448165894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6529363393783569},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6352683901786804},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6124045252799988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6049957275390625},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5515475273132324},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5480790138244629},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5389917492866516},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5239858031272888},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4691770374774933},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4645061194896698},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44194793701171875},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.4249448776245117},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.41693830490112305},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.1596372425556183},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11318546533584595},{"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":3,"locations":[{"id":"doi:10.3390/rs15133241","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15133241","pdf_url":"https://www.mdpi.com/2072-4292/15/13/3241/pdf?version=1687680014","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e4c1e01a000641ec975f18a75f6ece58","is_oa":true,"landing_page_url":"https://doaj.org/article/e4c1e01a000641ec975f18a75f6ece58","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 13, p 3241 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/13/3241/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15133241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 15; Issue 13; Pages: 3241","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15133241","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15133241","pdf_url":"https://www.mdpi.com/2072-4292/15/13/3241/pdf?version=1687680014","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7400000095367432,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4382137977.pdf"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W946160360","https://openalex.org/W1594031697","https://openalex.org/W1905063986","https://openalex.org/W1978617972","https://openalex.org/W1981095698","https://openalex.org/W1998651992","https://openalex.org/W2022014145","https://openalex.org/W2043035143","https://openalex.org/W2044609898","https://openalex.org/W2045718793","https://openalex.org/W2089182890","https://openalex.org/W2095466094","https://openalex.org/W2099590928","https://openalex.org/W2101678239","https://openalex.org/W2132424470","https://openalex.org/W2140908571","https://openalex.org/W2144552105","https://openalex.org/W2144994528","https://openalex.org/W2149631607","https://openalex.org/W2156909104","https://openalex.org/W2167129326","https://openalex.org/W2167433624","https://openalex.org/W2178424734","https://openalex.org/W2419285013","https://openalex.org/W2477957191","https://openalex.org/W2527372324","https://openalex.org/W2531168480","https://openalex.org/W2564730549","https://openalex.org/W2589306140","https://openalex.org/W2609570670","https://openalex.org/W2613751010","https://openalex.org/W2793927960","https://openalex.org/W2795353886","https://openalex.org/W2799735832","https://openalex.org/W2888785491","https://openalex.org/W2895942690","https://openalex.org/W2898446904","https://openalex.org/W2904187459","https://openalex.org/W2905175840","https://openalex.org/W2914168391","https://openalex.org/W2914272072","https://openalex.org/W2922390763","https://openalex.org/W2955244127","https://openalex.org/W2961668970","https://openalex.org/W2964287450","https://openalex.org/W2968310321","https://openalex.org/W2969351098","https://openalex.org/W2969832320","https://openalex.org/W2969945043","https://openalex.org/W2972199278","https://openalex.org/W2974691208","https://openalex.org/W2977400665","https://openalex.org/W2983485297","https://openalex.org/W2998655911","https://openalex.org/W3010350674","https://openalex.org/W3014372673","https://openalex.org/W3024860805","https://openalex.org/W3043490760","https://openalex.org/W3043805921","https://openalex.org/W3045585619","https://openalex.org/W3046668975","https://openalex.org/W3047472942","https://openalex.org/W3076936325","https://openalex.org/W3085907610","https://openalex.org/W3098810090","https://openalex.org/W3132264409","https://openalex.org/W3172232123","https://openalex.org/W3186140930","https://openalex.org/W3193255608","https://openalex.org/W3206389744","https://openalex.org/W4206278361","https://openalex.org/W4220774711","https://openalex.org/W4308744608","https://openalex.org/W4319301231","https://openalex.org/W4327518255","https://openalex.org/W6685885980","https://openalex.org/W6750415941","https://openalex.org/W6777571390","https://openalex.org/W6850187932"],"related_works":["https://openalex.org/W1487671059","https://openalex.org/W2084145074","https://openalex.org/W2160357252","https://openalex.org/W4206417246","https://openalex.org/W2905390890","https://openalex.org/W2800266504","https://openalex.org/W2090253159","https://openalex.org/W2969881582","https://openalex.org/W4390107508","https://openalex.org/W2073959551"],"abstract_inverted_index":{"High-resolution":[0],"multispectral":[1,34,45,70],"remote":[2],"sensing":[3],"images":[4,131],"offer":[5],"valuable":[6],"information":[7],"about":[8],"various":[9],"land":[10,115,118,295,298],"features,":[11],"providing":[12],"essential":[13],"details":[14],"and":[15,59,84,92,107,117,128,141,153,170,182,189,210,223,233,256,291,297],"spatially":[16],"accurate":[17],"representations.":[18],"In":[19],"the":[20,31,41,67,93,111,165,174,199,242,246,254,264,275,279,286],"complex":[21],"urban":[22],"environment,":[23],"classification":[24,42,112,122,175,216],"accuracy":[25,43,218],"is":[26],"not":[27],"often":[28],"adequate":[29],"using":[30,135],"complete":[32],"original":[33],"bands":[35,266],"for":[36,89,98,110,125,206,221,225,231,235,294],"practical":[37],"applications.":[38],"To":[39],"improve":[40],"of":[44,69,113,164,202,219,250,288],"images,":[46],"band":[47,239],"reduction":[48],"techniques":[49,293],"are":[50],"used,":[51],"which":[52],"can":[53],"be":[54],"categorized":[55],"into":[56,303],"feature":[57,60,90,99,280,289],"extraction":[58,290],"selection":[61,292],"techniques.":[62],"The":[63,121,162,187,259,282],"present":[64],"study":[65],"examined":[66],"use":[68,116,296],"satellite":[71,130],"bands,":[72],"spectral":[73],"indices":[74],"(including":[75],"Normalized":[76,80,85],"Difference":[77,81,86],"Built-up":[78],"Index,":[79,83],"Vegetation":[82],"Water":[87],"Index)":[88],"extraction,":[91],"principal":[94],"component":[95],"analysis":[96,201,261],"technique":[97],"selection.":[100],"These":[101],"methods":[102],"were":[103,192,267],"analyzed":[104],"both":[105,207,253],"independently":[106],"in":[108,132,198,237,252,272,306],"combination":[109],"multiple":[114],"cover":[119,299],"features.":[120],"was":[123,167],"performed":[124],"Landsat":[126,208,222,232,255],"9":[127,209],"Sentinel-2":[129,211,236],"Delhi,":[133],"India,":[134],"six":[136],"machine":[137],"learning":[138],"techniques:":[139],"Classification":[140],"Regression":[142],"Tree,":[143],"Minimum":[144],"Distance,":[145],"Naive":[146],"Bayes,":[147],"Random":[148],"Forest,":[149],"Gradient":[150],"Tree":[151],"Boosting,":[152],"Support":[154],"Vector":[155],"Machine":[156],"on":[157],"Google":[158],"Earth":[159],"Engine":[160],"platform.":[161],"performance":[163],"classifiers":[166,191,271],"evaluated":[168],"quantitatively":[169],"qualitatively":[171],"to":[172,194,270,285],"analyze":[173],"results":[176],"with":[177],"whole":[178],"image":[179],"(comprehensive":[180],"feature)":[181],"small":[183],"subset":[184],"(targeted":[185],"feature).":[186],"RF":[188,213,243],"GTB":[190,228],"found":[193],"outperform":[195],"all":[196,203,238],"others":[197],"quantitative":[200],"input":[204],"combinations":[205],"datasets.":[212,258],"achieved":[214,229,245],"a":[215],"total":[217],"96.19%":[220],"96.95%":[224],"Sentinel-2,":[226],"whereas":[227],"91.62%":[230],"92.89%":[234],"combinations.":[240],"Furthermore,":[241],"classifier":[244],"highest":[247],"F1":[248],"score":[249],"0.97":[251],"Sentinel":[257],"qualitative":[260],"revealed":[262],"that":[263],"PCA":[265],"particularly":[268],"useful":[269],"distinguishing":[273],"even":[274],"slightest":[276],"differences":[277],"among":[278],"class.":[281],"findings":[283],"contribute":[284],"understanding":[287],"classification,":[300],"offering":[301],"insights":[302],"their":[304],"effectiveness":[305],"different":[307],"scenarios.":[308]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}