{"id":"https://openalex.org/W2769143033","doi":"https://doi.org/10.1109/lgrs.2017.2765339","title":"Hyperspectral Band Selection Based on Deep Convolutional Neural Network and Distance Density","display_name":"Hyperspectral Band Selection Based on Deep Convolutional Neural Network and Distance Density","publication_year":2017,"publication_date":"2017-11-16","ids":{"openalex":"https://openalex.org/W2769143033","doi":"https://doi.org/10.1109/lgrs.2017.2765339","mag":"2769143033"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2017.2765339","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2017.2765339","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5102996528","display_name":"Ying Zhan","orcid":"https://orcid.org/0000-0002-5768-6184"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ying Zhan","raw_affiliation_strings":["College of Information Science and Technology, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101517880","display_name":"Dan Hu","orcid":"https://orcid.org/0000-0002-4437-6339"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Hu","raw_affiliation_strings":["College of Information Science and Technology, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020590703","display_name":"Haihua Xing","orcid":"https://orcid.org/0009-0006-2976-7426"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haihua Xing","raw_affiliation_strings":["College of Information Science and Technology, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101778276","display_name":"Xianchuan Yu","orcid":"https://orcid.org/0000-0002-1425-0751"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianchuan Yu","raw_affiliation_strings":["College of Information Science and Technology, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102996528"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":8.0388,"has_fulltext":false,"cited_by_count":90,"citation_normalized_percentile":{"value":0.97717511,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"14","issue":"12","first_page":"2365","last_page":"2369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9966999888420105,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.992900013923645,"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":0.8537898063659668},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7558624744415283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7107120156288147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.666651725769043},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5999411940574646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5852463245391846},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4897141754627228},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4410943388938904},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42543551325798035},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4125097990036011}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8537898063659668},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7558624744415283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7107120156288147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.666651725769043},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5999411940574646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5852463245391846},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4897141754627228},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4410943388938904},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42543551325798035},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4125097990036011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2017.2765339","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2017.2765339","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5176970212","display_name":null,"funder_award_id":"41672323","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6701166227","display_name":null,"funder_award_id":"11471045","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G745144014","display_name":null,"funder_award_id":"41272359","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7700551107","display_name":null,"funder_award_id":"20156227","funder_id":"https://openalex.org/F4320322866","funder_display_name":"Natural Science Foundation of Hainan Province"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322866","display_name":"Natural Science Foundation of Hainan Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1910604087","https://openalex.org/W2042294722","https://openalex.org/W2078491260","https://openalex.org/W2138038253","https://openalex.org/W2150566919","https://openalex.org/W2150990614","https://openalex.org/W2179290474","https://openalex.org/W2292865806","https://openalex.org/W2294445779","https://openalex.org/W2343745409","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2516282711","https://openalex.org/W2571192787","https://openalex.org/W2572810799","https://openalex.org/W2773352017","https://openalex.org/W2919115771"],"related_works":["https://openalex.org/W2132083814","https://openalex.org/W2292979300","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W4391160746","https://openalex.org/W3133861977","https://openalex.org/W1552543208","https://openalex.org/W2074396517"],"abstract_inverted_index":{"In":[0],"this":[1],"letter,":[2],"a":[3,41,47,76,110,121],"band-selection":[4],"approach":[5],"based":[6,55,125],"on":[7,56,126,142],"the":[8,24,35,59,65,69,83,88,91,94,98,102,119,133,136,149],"deep":[9],"convolutional":[10],"neural":[11],"network":[12],"(CNN)":[13],"and":[14],"distance":[15],"density":[16],"(DD)":[17],"is":[18,64,72,109,128],"proposed.":[19,130],"This":[20,106],"method":[21,92,124,151],"effectively":[22,81],"mitigates":[23],"curse":[25],"of":[26,68,135],"dimensionality":[27],"for":[28,113],"hyperspectral":[29,36],"images":[30],"(HSIs).":[31],"First,":[32],"we":[33,51,79],"use":[34],"full-band":[37],"data":[38,122],"to":[39,45],"train":[40],"custom":[42],"1-D":[43],"CNN":[44,70],"obtain":[46],"well-trained":[48],"model.":[49,89],"Second,":[50],"select":[52],"band":[53,84,95,114],"combinations":[54,85,96],"DD.":[57],"Using":[58],"rectified":[60],"linear":[61],"unit,":[62],"which":[63],"activation":[66],"function":[67],"that":[71,148],"only":[73],"activated":[74],"with":[75,97],"nonzero":[77],"value,":[78],"can":[80,152],"test":[82],"without":[86],"retraining":[87],"Finally,":[90],"selects":[93],"highest":[99],"precision":[100,107],"as":[101],"final":[103],"selected":[104],"bands.":[105],"measure":[108],"new":[111],"criterion":[112],"selection.":[115],"To":[116,131],"further":[117],"improve":[118],"performance,":[120],"augmentation":[123],"DD":[127],"also":[129],"justify":[132],"effectiveness":[134],"proposed":[137,150],"method,":[138],"experiments":[139],"are":[140],"conducted":[141],"two":[143],"HSIs.":[144],"The":[145],"results":[146,156],"show":[147],"acquire":[153],"more":[154],"satisfactory":[155],"than":[157],"traditional":[158],"methods.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":7}],"updated_date":"2026-03-04T09:10:02.777135","created_date":"2025-10-10T00:00:00"}