{"id":"https://openalex.org/W4391935832","doi":"https://doi.org/10.1109/tgrs.2024.3367127","title":"Iterative Gaussian\u2013Laplacian Pyramid Network for Hyperspectral Image Classification","display_name":"Iterative Gaussian\u2013Laplacian Pyramid Network for Hyperspectral Image Classification","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391935832","doi":"https://doi.org/10.1109/tgrs.2024.3367127"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3367127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3367127","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5073412670","display_name":"Chein\u2010I Chang","orcid":"https://orcid.org/0000-0002-5450-4891"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]},{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["CN","TW","US"],"is_corresponding":false,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China,","Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5450-4891","affiliations":[{"raw_affiliation_string":"Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China,","institution_ids":["https://openalex.org/I43313876"]},{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010533078","display_name":"Chia-Chen Liang","orcid":"https://orcid.org/0009-0008-3245-9795"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chia-Chen Liang","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0009-0008-3245-9795","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008114238","display_name":"Peter Hu","orcid":"https://orcid.org/0000-0001-7332-758X"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I1315496137","display_name":"University of Maryland Medical Center","ror":"https://ror.org/00sde4n60","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1315496137","https://openalex.org/I14285509"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Fuming Hu","raw_affiliation_strings":["Department of Anesthesia, R Adams Cowley Shock Trauma Center, Shock Trauma Anesthesia Organized Research Center, University of Maryland School of Medicine, Baltimore, MD, USA","Department of Anesthesia, R. A. Cowley Shock Trauma Center, Shock Trauma Anesthesia Organized Research Center, University of Maryland School of Medicine, Baltimore, Maryland, USA"],"raw_orcid":"https://orcid.org/0000-0001-7332-758X","affiliations":[{"raw_affiliation_string":"Department of Anesthesia, R Adams Cowley Shock Trauma Center, Shock Trauma Anesthesia Organized Research Center, University of Maryland School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593","https://openalex.org/I1315496137"]},{"raw_affiliation_string":"Department of Anesthesia, R. A. Cowley Shock Trauma Center, Shock Trauma Anesthesia Organized Research Center, University of Maryland School of Medicine, Baltimore, Maryland, USA","institution_ids":["https://openalex.org/I126744593"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.4986,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.93185442,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.978600025177002,"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":0.978600025177002,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8729507923126221},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5585452318191528},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49862194061279297},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49422982335090637},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4867825210094452},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.4788122773170471},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4503088593482971},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.39507585763931274},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3658207952976227},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3495197296142578},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3383655250072479},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20948830246925354},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09854108095169067}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8729507923126221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5585452318191528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49862194061279297},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49422982335090637},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4867825210094452},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.4788122773170471},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4503088593482971},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.39507585763931274},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3658207952976227},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3495197296142578},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3383655250072479},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20948830246925354},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09854108095169067},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"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/tgrs.2024.3367127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3367127","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G135160501","display_name":null,"funder_award_id":"111-2634-F-006-012","funder_id":"https://openalex.org/F4320331164","funder_display_name":"National Science and Technology Council"}],"funders":[{"id":"https://openalex.org/F4320331164","display_name":"National Science and Technology Council","ror":"https://ror.org/00wnb9798"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W2049694710","https://openalex.org/W2103384342","https://openalex.org/W2103504761","https://openalex.org/W2125188192","https://openalex.org/W2165755981","https://openalex.org/W2166923144","https://openalex.org/W2169369279","https://openalex.org/W2608308670","https://openalex.org/W2754507318","https://openalex.org/W2768309288","https://openalex.org/W2789883140","https://openalex.org/W2791725003","https://openalex.org/W2888119354","https://openalex.org/W2890945279","https://openalex.org/W2899937451","https://openalex.org/W2901920630","https://openalex.org/W2907064575","https://openalex.org/W2914331134","https://openalex.org/W2942454403","https://openalex.org/W2971007343","https://openalex.org/W2991616716","https://openalex.org/W2991916973","https://openalex.org/W3005261807","https://openalex.org/W3013868170","https://openalex.org/W3023351371","https://openalex.org/W3041591456","https://openalex.org/W3089820912","https://openalex.org/W3114720220","https://openalex.org/W3122017985","https://openalex.org/W3133055443","https://openalex.org/W3136140595","https://openalex.org/W3140678646","https://openalex.org/W3165209762","https://openalex.org/W3181896199","https://openalex.org/W3186823202","https://openalex.org/W3199303234","https://openalex.org/W3205033505","https://openalex.org/W4205102943","https://openalex.org/W4248253651","https://openalex.org/W4249700702","https://openalex.org/W4285117094","https://openalex.org/W4285155962","https://openalex.org/W4285303509","https://openalex.org/W4312455041","https://openalex.org/W4378364888","https://openalex.org/W4385489621"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2070598848","https://openalex.org/W4313014865","https://openalex.org/W2019190440"],"abstract_inverted_index":{"Gaussian":[0,26,133,138],"pyramid":[1,16,38,59,159],"(GP)":[2],"is":[3,18,231],"a":[4,15,21,36,65,77,83,90,95,108,114,131,137,141,149,204,207,227],"commonly":[5],"used":[6],"image":[7,13,154],"coding":[8],"technique":[9],"which":[10,17,162],"encodes":[11],"an":[12,187,195],"as":[14,136,203],"stacked":[19],"by":[20,103,113,130,185,216],"set":[22],"of":[23,54,76,79,144,206],"images":[24,47,49],"with":[25,34,219],"window-reduced":[27],"sizes":[28],"and":[29,67,94,105,124,240],"multiple":[30],"spatial":[31],"resolutions.":[32],"Associated":[33],"GP":[35,118,166],"Laplacian":[37],"(LP)":[39],"can":[40,110,126,193,200,223],"be":[41,111,127,201],"also":[42,171,241],"constructed":[43],"to":[44,152,167,173,247],"represent":[45],"differential":[46,175],"between":[48,177],"in":[50,64,82,107,117,119,140,226],"two":[51,178],"consecutive":[52,179],"layers":[53,80,180],"GP.":[55,145],"Such":[56],"resulting":[57],"Gaussian-Laplacian":[58,158],"(GLP)":[60],"performs":[61],"data":[62],"compression":[63],"lossless":[66],"lossy":[68],"manner.":[69],"A":[70],"convolutional":[71,91],"neural":[72],"network":[73,160],"(CNN)":[74],"consists":[75],"series":[78],"concatenated":[81],"feedforward":[84],"manner":[85],"where":[86],"each":[87,100],"layer":[88,101,116,143],"has":[89],"sublayer":[92,97],"(CL)":[93],"pooling":[96],"(PL).":[98],"Interestingly,":[99],"implemented":[102],"CL":[104,123],"PL":[106,125],"CNN":[109,182,214,218,225],"realized":[112],"single":[115,142],"the":[120],"sense":[121],"that":[122,181,199,233],"carried":[128],"out":[129],"low-pass":[132],"filter":[134],"operated":[135],"kernel":[139],"This":[146],"paper":[147],"develops":[148],"new":[150],"approach":[151],"hyperspectral":[153],"classification":[155],"(HSIC),":[156],"called":[157],"(GLPN)":[161],"uses":[163],"not":[164],"only":[165],"realize":[168,224],"CNN,":[169],"but":[170],"LP":[172],"capture":[174],"information":[176],"cannot.":[183],"Furthermore,":[184],"incorporating":[186],"iterative":[188,196,210],"process":[189],"into":[190],"GLPN":[191,197,222],"we":[192],"derive":[194],"(IGLPN)":[198],"considered":[202],"companion":[205],"recently":[208],"developed":[209],"random":[211],"training":[212],"sampling":[213],"(IRTS-CNN)":[215],"replacing":[217],"GLPN.":[220],"Since":[221],"better":[228,237],"way,":[229],"it":[230],"expected":[232],"IGLPN":[234],"will":[235],"perform":[236],"than":[238],"IRTS-CNN":[239],"significantly":[242],"reduce":[243],"computational":[244],"efficiency":[245],"compared":[246],"IRTS-CNN.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
