{"id":"https://openalex.org/W4389546877","doi":"https://doi.org/10.3390/rs15245679","title":"Tree Species Classification from Airborne Hyperspectral Images Using Spatial\u2013Spectral Network","display_name":"Tree Species Classification from Airborne Hyperspectral Images Using Spatial\u2013Spectral Network","publication_year":2023,"publication_date":"2023-12-10","ids":{"openalex":"https://openalex.org/W4389546877","doi":"https://doi.org/10.3390/rs15245679"},"language":"en","primary_location":{"id":"doi:10.3390/rs15245679","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15245679","pdf_url":"https://www.mdpi.com/2072-4292/15/24/5679/pdf?version=1702192988","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/2072-4292/15/24/5679/pdf?version=1702192988","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111090587","display_name":"Chengchao Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengchao Hou","raw_affiliation_strings":["Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China"],"affiliations":[{"raw_affiliation_string":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100638009","display_name":"Zhengjun Liu","orcid":"https://orcid.org/0000-0002-0303-6290"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengjun Liu","raw_affiliation_strings":["Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China"],"affiliations":[{"raw_affiliation_string":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100333353","display_name":"Yiming Chen","orcid":"https://orcid.org/0000-0002-1408-5194"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiming Chen","raw_affiliation_strings":["Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China"],"affiliations":[{"raw_affiliation_string":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090868443","display_name":"Shuo Wang","orcid":"https://orcid.org/0000-0001-7018-3971"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Wang","raw_affiliation_strings":["Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China"],"affiliations":[{"raw_affiliation_string":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101687873","display_name":"Aixia Liu","orcid":"https://orcid.org/0000-0002-8115-8234"},"institutions":[{"id":"https://openalex.org/I4210092591","display_name":"China Centre for Resources Satellite Data and Application","ror":"https://ror.org/00ft0fw96","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210092591"]},{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aixia Liu","raw_affiliation_strings":["Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"],"affiliations":[{"raw_affiliation_string":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China","institution_ids":["https://openalex.org/I4210092591","https://openalex.org/I211433327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100333353"],"corresponding_institution_ids":["https://openalex.org/I4210114963"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":3.0625,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.91267653,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"15","issue":"24","first_page":"5679","last_page":"5679"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987000226974487,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9955000281333923,"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":0.8588079214096069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.72088223695755},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5996543169021606},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5807145833969116},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5345441699028015},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5218621492385864},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5125977396965027},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.47980716824531555},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.45575278997421265},{"id":"https://openalex.org/keywords/fusion-mechanism","display_name":"Fusion mechanism","score":0.4537404775619507},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3571789860725403},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.29347091913223267},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14097091555595398},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09757032990455627}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8588079214096069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72088223695755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5996543169021606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5807145833969116},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5345441699028015},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5218621492385864},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5125977396965027},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.47980716824531555},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.45575278997421265},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.4537404775619507},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3571789860725403},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.29347091913223267},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14097091555595398},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09757032990455627},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C103038307","wikidata":"https://www.wikidata.org/wiki/Q6556360","display_name":"Lipid bilayer fusion","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs15245679","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15245679","pdf_url":"https://www.mdpi.com/2072-4292/15/24/5679/pdf?version=1702192988","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:897da6d410b6482c9fec24b6283e803e","is_oa":true,"landing_page_url":"https://doaj.org/article/897da6d410b6482c9fec24b6283e803e","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 24, p 5679 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs15245679","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15245679","pdf_url":"https://www.mdpi.com/2072-4292/15/24/5679/pdf?version=1702192988","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":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4389546877.pdf"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2027001053","https://openalex.org/W2059217921","https://openalex.org/W2194775991","https://openalex.org/W2515306179","https://openalex.org/W2560676219","https://openalex.org/W2577238056","https://openalex.org/W2613718673","https://openalex.org/W2752782242","https://openalex.org/W2802651079","https://openalex.org/W2804458818","https://openalex.org/W2817057931","https://openalex.org/W2884585870","https://openalex.org/W2911261286","https://openalex.org/W2948157022","https://openalex.org/W2955077228","https://openalex.org/W2956102952","https://openalex.org/W2963446712","https://openalex.org/W2963495494","https://openalex.org/W2974254117","https://openalex.org/W2976210365","https://openalex.org/W2979348177","https://openalex.org/W2984152560","https://openalex.org/W2991616716","https://openalex.org/W2992027343","https://openalex.org/W3000415744","https://openalex.org/W3036016333","https://openalex.org/W3083808601","https://openalex.org/W3088522164","https://openalex.org/W3121566766","https://openalex.org/W3128868023","https://openalex.org/W3132859298","https://openalex.org/W3139003984","https://openalex.org/W3140854437","https://openalex.org/W3164449918","https://openalex.org/W3166623178","https://openalex.org/W3166716987","https://openalex.org/W3183451374","https://openalex.org/W3197961809","https://openalex.org/W3205033505","https://openalex.org/W4210794570","https://openalex.org/W4221065202","https://openalex.org/W4229080700","https://openalex.org/W4283829529","https://openalex.org/W4285180452","https://openalex.org/W4293253007","https://openalex.org/W4297920830","https://openalex.org/W4312443924","https://openalex.org/W4313524854","https://openalex.org/W4366609679","https://openalex.org/W4385627268","https://openalex.org/W6768243097","https://openalex.org/W6808400268","https://openalex.org/W6840044108","https://openalex.org/W6848016077","https://openalex.org/W6856043894"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"Tree":[0],"species":[1,18,70,81,164,291],"identification":[2],"is":[3,20,142,251],"a":[4,57,184],"critical":[5,159],"component":[6],"of":[7,16,52,108,124,128,134,149,170,186,240,247,255],"forest":[8,25,315],"resource":[9,28],"monitoring,":[10],"and":[11,13,27,36,39,97,115,118,201,223,226,243,272,300,313],"timely":[12],"accurate":[14],"acquisition":[15],"tree":[17,45,69,80,163,218,241,290],"information":[19,38,127,225],"the":[21,50,64,88,93,100,125,132,138,145,150,154,168,171,207,211,228,233,237,244,248,256,264,301],"basis":[22],"for":[23,68,161],"sustainable":[24],"management":[26],"assessment.":[29],"Airborne":[30],"hyperspectral":[31,53,298],"images":[32,299],"have":[33,308],"rich":[34],"spectral":[35,111,224],"spatial":[37,113,222],"can":[40,235,293],"detect":[41],"subtle":[42],"differences":[43],"among":[44],"species.":[46],"To":[47,166],"fully":[48],"utilize":[49],"advantages":[51],"images,":[54],"we":[55,174],"propose":[56],"double-branch":[58,249],"spatial\u2013spectral":[59,126],"joint":[60],"network":[61,106,151,234,250],"based":[62],"on":[63,78],"SimAM":[65,139,229],"attention":[66,140,230],"mechanism":[67,141,231],"classification.":[71,165],"This":[72,285],"method":[73,177,209,262],"achieved":[74,210,295],"high":[75],"classification":[76,181,213,238,245,292],"accuracy":[77,214,239,266],"three":[79,109,217],"datasets":[82],"(93.31%":[83],"OA":[84],"value":[85],"obtained":[86],"in":[87,92,99,215,304,311],"TEF":[89],"dataset,":[90,96],"95.7%":[91],"Tiegang":[94],"Reservoir":[95],"98.82%":[98],"Xiongan":[101],"New":[102],"Area":[103],"dataset).":[104],"The":[105,188,260],"consists":[107],"parts:":[110],"branch,":[112,114],"feature":[116,146],"fusion,":[117],"both":[119],"branches":[120],"make":[121],"full":[122],"use":[123],"pixels":[129],"to":[130,144,152,156],"avoid":[131],"loss":[133],"information.":[135],"In":[136],"addition,":[137],"added":[143],"fusion":[147],"part":[148],"refine":[153],"features":[155,160],"extract":[157],"more":[158],"high-precision":[162,289],"validate":[167],"robustness":[169],"proposed":[172,208,261,303],"method,":[173],"compared":[175],"this":[176,305],"with":[178,194,277],"other":[179,202],"advanced":[180],"methods":[182,198,302],"through":[183],"series":[185],"experiments.":[187],"results":[189],"show":[190],"that:":[191],"(1)":[192],"Compared":[193],"traditional":[195],"machine":[196],"learning":[197,205],"(SVM,":[199],"RF)":[200],"state-of-the-art":[203],"deep":[204],"methods,":[206],"highest":[212,265],"all":[216],"datasets.":[219],"(2)":[220],"Combining":[221],"incorporating":[227],"into":[232],"improve":[236],"species,":[242],"performance":[246],"better":[252],"than":[253],"that":[254,288],"single-branch":[257],"network.":[258],"(3)":[259],"obtains":[263],"under":[267],"different":[268,278],"training":[269,279],"sample":[270,280],"proportions,":[271,281],"does":[273],"not":[274],"change":[275],"significantly":[276],"which":[282,307],"are":[283],"stable.":[284],"study":[286],"demonstrates":[287],"be":[294],"using":[296],"airborne":[297],"study,":[306],"great":[309],"potential":[310],"investigating":[312],"monitoring":[314],"resources.":[316]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7}],"updated_date":"2026-02-28T09:26:25.869077","created_date":"2023-12-12T00:00:00"}
