{"id":"https://openalex.org/W4312451588","doi":"https://doi.org/10.1109/igarss46834.2022.9884492","title":"Classification of an 8-Band Multi-Spectral Dataset Using DCNNs with Weight Initializations Derived from Pre-Trained RGB Networks","display_name":"Classification of an 8-Band Multi-Spectral Dataset Using DCNNs with Weight Initializations Derived from Pre-Trained RGB Networks","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312451588","doi":"https://doi.org/10.1109/igarss46834.2022.9884492"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884492","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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/A5060479557","display_name":"Trevor M. Bajkowski","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Trevor M. Bajkowski","raw_affiliation_strings":["University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009311723","display_name":"J. Alex Hurt","orcid":"https://orcid.org/0000-0002-7234-1301"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Alex Hurt","raw_affiliation_strings":["University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079391589","display_name":"Curt H. Davis","orcid":"https://orcid.org/0000-0002-5781-0931"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Curt H. Davis","raw_affiliation_strings":["University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051712346","display_name":"Grant J. Scott","orcid":"https://orcid.org/0000-0001-5870-9387"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Grant J. Scott","raw_affiliation_strings":["University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,Dept. of Electrical Engineering &#x0026; Computer Science,Columbia,MO,USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7091,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71773445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"abs 2104 298","issue":null,"first_page":"187","last_page":"190"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9980999827384949,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.8685638904571533},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.8165781497955322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7131183743476868},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6964284777641296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6889515519142151},{"id":"https://openalex.org/keywords/spectral-bands","display_name":"Spectral bands","score":0.681162416934967},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.529691755771637},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47406238317489624},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.46415719389915466},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4176945090293884},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3864303231239319},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3781132400035858},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25670313835144043},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1298556923866272},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10752061009407043}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8685638904571533},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8165781497955322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7131183743476868},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6964284777641296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6889515519142151},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.681162416934967},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.529691755771637},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47406238317489624},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46415719389915466},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4176945090293884},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3864303231239319},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3781132400035858},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25670313835144043},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1298556923866272},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10752061009407043},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884492","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884492","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2755465803","https://openalex.org/W2811199523","https://openalex.org/W2887751143","https://openalex.org/W2955425717","https://openalex.org/W2963785576","https://openalex.org/W2964350391","https://openalex.org/W3122874210","https://openalex.org/W3138082085","https://openalex.org/W3145444543","https://openalex.org/W4297670610","https://openalex.org/W4308831279","https://openalex.org/W6694260854","https://openalex.org/W6748130322","https://openalex.org/W6754073629","https://openalex.org/W6756444276","https://openalex.org/W6788610376","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W2792279927","https://openalex.org/W4385497869","https://openalex.org/W283587633","https://openalex.org/W2084145074","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2160357252","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305"],"abstract_inverted_index":{"Modern":[0],"satellite":[1],"sensors":[2],"can":[3,30,55,92],"capture":[4],"reflected":[5],"optical":[6],"energy":[7,25],"from":[8,26,77,114],"wavelengths":[9,129],"well":[10,21],"outside":[11],"of":[12,15,69,80,97,124,152,173],"the":[13,27,75,84,95,115,132,150,200],"range":[14],"human":[16],"perception.":[17],"Indeed,":[18],"it":[19],"is":[20,43,47],"known":[22],"that":[23,82,86,154,181],"electromagnetic":[24],"\u201cnon-visible\u201d":[28],"spectrum":[29],"be":[31,56],"used":[32,136],"to":[33,58],"identify":[34],"materials":[35],"in":[36,60,107,137],"geological,":[37],"oceanographic,":[38],"and":[39,49,103,143,170],"agricultural":[40],"contexts.":[41],"What":[42],"less":[44],"understood,":[45],"however,":[46],"if":[48],"how":[50],"this":[51,146,174],"additional":[52],"spectral":[53,99,125],"information":[54],"leveraged":[57],"aid":[59],"difficult":[61],"computer":[62,138],"vision":[63,139],"tasks,":[64],"e.g.,":[65],"classification":[66],"or":[67],"detection":[68],"man-made":[70],"objects.":[71],"Thus,":[72],"we":[73,110,148],"present":[74],"results":[76,151,179],"a":[78],"series":[79],"experiments":[81,153],"evaluate":[83],"benefits":[85],"Deep":[87],"Convolutional":[88],"Neural":[89],"Networks":[90],"(DCNN)":[91],"garner":[93],"through":[94],"inclusion":[96],"more":[98],"information.":[100],"For":[101],"training":[102],"testing":[104,189],"these":[105],"networks":[106,182,197],"image":[108,112],"classification,":[109],"extract":[111],"tiles":[113],"xView":[116],"multi-spectral":[117],"dataset.":[118],"These":[119],"images":[120],"contain":[121],"eight":[122,175],"channels":[123],"data":[126],"including":[127],"five":[128],"bands":[130,134],"beyond":[131],"three":[133],"traditionally":[135],"researcher":[140],"(red,":[141],"green,":[142],"blue).":[144],"In":[145],"work,":[147],"report":[149],"use":[155],"an":[156,187],"80\u201320":[157],"train-test":[158],"split":[159],"with":[160,199],"four":[161],"DCNN":[162],"architectures":[163],"on":[164,184],"full":[165],"8-band":[166],"Multispectral":[167],"Imagery":[168],"(MSI)":[169],"various":[171],"subsets":[172],"banded":[176],"imagery.":[177],"The":[178],"show":[180],"trained":[183,198],"MSI":[185],"have":[186],"average":[188],"F1-score":[190],"around":[191],"1.0":[192],"point":[193],"higher":[194],"than":[195],"RGB":[196],"same":[201],"methods.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
