{"id":"https://openalex.org/W3200742127","doi":"https://doi.org/10.1109/lgrs.2021.3112198","title":"Exploiting Spectral\u2013Spatial Information Using Deep Random Forest for Hyperspectral Imagery Classification","display_name":"Exploiting Spectral\u2013Spatial Information Using Deep Random Forest for Hyperspectral Imagery Classification","publication_year":2021,"publication_date":"2021-09-22","ids":{"openalex":"https://openalex.org/W3200742127","doi":"https://doi.org/10.1109/lgrs.2021.3112198","mag":"3200742127"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3112198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3112198","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/A5077048862","display_name":"Fei Tong","orcid":"https://orcid.org/0000-0001-6056-9299"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fei Tong","raw_affiliation_strings":["Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB, Canada"],"raw_orcid":"https://orcid.org/0000-0001-6056-9299","affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB, Canada","institution_ids":["https://openalex.org/I106938459"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100356784","display_name":"Yun Zhang","orcid":"https://orcid.org/0000-0001-9231-0142"},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yun Zhang","raw_affiliation_strings":["Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9231-0142","affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB, Canada","institution_ids":["https://openalex.org/I106938459"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I106938459"],"apc_list":null,"apc_paid":null,"fwci":1.8984,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.87329539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"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":0.9950000047683716,"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":0.9735999703407288,"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":0.7705956101417542},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7415773272514343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7344669103622437},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7213087677955627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7206377387046814},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6379683017730713},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.6298239231109619},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5755707621574402},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5689430832862854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49706605076789856},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.48014184832572937},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3382987082004547},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29035383462905884},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2571476101875305},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07687744498252869}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7705956101417542},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7415773272514343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7344669103622437},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7213087677955627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7206377387046814},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6379683017730713},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.6298239231109619},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5755707621574402},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5689430832862854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49706605076789856},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.48014184832572937},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3382987082004547},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29035383462905884},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2571476101875305},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07687744498252869}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3112198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3112198","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":[{"score":0.6800000071525574,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G3476830129","display_name":null,"funder_award_id":"19FANBA36","funder_id":"https://openalex.org/F4320334436","funder_display_name":"Canadian Space Agency"}],"funders":[{"id":"https://openalex.org/F4320334436","display_name":"Canadian Space Agency","ror":"https://ror.org/03a1gte98"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1950365613","https://openalex.org/W1972814520","https://openalex.org/W2001298023","https://openalex.org/W2009286595","https://openalex.org/W2056132907","https://openalex.org/W2059110141","https://openalex.org/W2085529604","https://openalex.org/W2097915756","https://openalex.org/W2105386417","https://openalex.org/W2128550928","https://openalex.org/W2135431554","https://openalex.org/W2136251662","https://openalex.org/W2164437025","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2518815253","https://openalex.org/W2572303978","https://openalex.org/W2603834682","https://openalex.org/W2745791577","https://openalex.org/W2768309288","https://openalex.org/W2793330652","https://openalex.org/W2808098982","https://openalex.org/W2896669914","https://openalex.org/W2902193101","https://openalex.org/W3105357426","https://openalex.org/W3151633797","https://openalex.org/W4232714830","https://openalex.org/W4233367343"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W4376166922","https://openalex.org/W2490526372","https://openalex.org/W4221142204"],"abstract_inverted_index":{"In":[0,50,95],"recent":[1],"years,":[2],"deep":[3,20,22,31,106],"learning":[4],"methods":[5],"have":[6],"been":[7],"widely":[8],"applied":[9],"to":[10,45,63,89,119,139,159,176],"hyperspectral":[11],"image":[12,54],"(HSI)":[13],"classification.":[14,179],"Besides":[15],"convolutional":[16],"neural":[17],"network":[18],"(CNN)-based":[19],"learning,":[21],"random":[23,156],"forest":[24,157],"(RF)-based":[25],"method,":[26],"such":[27],"as":[28],"densely":[29],"connected":[30],"RF":[32,107,146],"(DCDRF),":[33],"was":[34],"also":[35],"developed":[36],"for":[37],"HSI":[38],"classification":[39,48,102,123,150,194],"which":[40,109],"utilized":[41,62],"the":[42,47,65,72,84,90,113,122,131,149,163,178,188],"spectral\u2013spatial":[43,105],"information":[44,67,74,115],"improve":[46,121],"accuracy.":[49,124],"DCDRF,":[51],"evenly":[52],"distributed":[53],"patches":[55],"with":[56],"a":[57,100],"fixed":[58],"patch":[59,77,85,133],"size":[60],"are":[61],"extract":[64],"spatial":[66,73,114,143],"of":[68,92,174],"ground":[69,93],"objects.":[70,94],"However,":[71],"in":[75,117,148],"each":[76],"is":[78,87,137,152,166],"not":[79],"always":[80],"correct,":[81],"especially":[82],"when":[83],"center":[86],"close":[88],"edge":[91],"this":[96],"letter,":[97],"we":[98],"propose":[99],"new":[101],"method":[103],"called":[104],"(SSDRF)":[108],"can":[110,191],"fully":[111],"utilize":[112],"existing":[116],"HSIs":[118,185],"further":[120],"The":[125,145,180],"joint":[126],"region":[127],"that":[128,187],"combines":[129],"both":[130],"fixed-size":[132],"and":[134,170,196],"shape-adaptive":[135],"superpixel":[136],"proposed":[138,189],"exploit":[140],"more":[141],"accurate":[142],"information.":[144],"used":[147],"model":[151],"replaced":[153],"by":[154],"extremely":[155],"(EF)":[158],"avoid":[160],"overfitting.":[161],"Moreover,":[162],"majority":[164],"voting":[165],"conducted":[167],"within":[168],"superpixels":[169,175],"among":[171],"different":[172],"scales":[173],"optimize":[177],"experimental":[181],"results":[182,195],"on":[183],"three":[184],"demonstrate":[186],"SSDRF":[190],"achieve":[192],"satisfactory":[193],"outperforms":[197],"patched-based":[198],"DCDRF.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
