{"id":"https://openalex.org/W4414477340","doi":"https://doi.org/10.3390/e27100995","title":"SGFNet: Redundancy-Reduced Spectral\u2013Spatial Fusion Network for Hyperspectral Image Classification","display_name":"SGFNet: Redundancy-Reduced Spectral\u2013Spatial Fusion Network for Hyperspectral Image Classification","publication_year":2025,"publication_date":"2025-09-24","ids":{"openalex":"https://openalex.org/W4414477340","doi":"https://doi.org/10.3390/e27100995","pmid":"https://pubmed.ncbi.nlm.nih.gov/41148953"},"language":"en","primary_location":{"id":"doi:10.3390/e27100995","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27100995","pdf_url":"https://www.mdpi.com/1099-4300/27/10/995/pdf?version=1758705160","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/27/10/995/pdf?version=1758705160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100383954","display_name":"Boyu Wang","orcid":"https://orcid.org/0000-0002-6108-3589"},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN","MO"],"is_corresponding":false,"raw_author_name":"Boyu Wang","raw_affiliation_strings":["Faculty of Innovation and Engineering, Macau University of Science and Technology, Taipa 999078, Macau","School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Innovation and Engineering, Macau University of Science and Technology, Taipa 999078, Macau","institution_ids":["https://openalex.org/I111950717"]},{"raw_affiliation_string":"School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100564557","display_name":"Chi Cao","orcid":"https://orcid.org/0009-0004-0610-211X"},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Chi Cao","raw_affiliation_strings":["Faculty of Innovation and Engineering, Macau University of Science and Technology, Taipa 999078, Macau"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Innovation and Engineering, Macau University of Science and Technology, Taipa 999078, Macau","institution_ids":["https://openalex.org/I111950717"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101478809","display_name":"De-Xing Kong","orcid":"https://orcid.org/0000-0003-3289-8954"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dexing Kong","raw_affiliation_strings":["School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101478809"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":1.5239,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.85832288,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"27","issue":"10","first_page":"995","last_page":"995"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9944999814033508,"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":0.9868999719619751,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8137999773025513},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.7914999723434448},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6894999742507935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5742999911308289},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5623999834060669},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4896000027656555},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.38760000467300415},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3434999883174896}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8137999773025513},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7914999723434448},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7192999720573425},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6894999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029000282287598},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5742999911308289},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5623999834060669},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4896000027656555},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3052999973297119},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2547000050544739},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e27100995","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27100995","pdf_url":"https://www.mdpi.com/1099-4300/27/10/995/pdf?version=1758705160","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},{"id":"pmid:41148953","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41148953","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:6264829a670b4006ac70d6daa4b96c42","is_oa":true,"landing_page_url":"https://doaj.org/article/6264829a670b4006ac70d6daa4b96c42","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 27, Iss 10, p 995 (2025)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:11371541","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12563197","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e27100995","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27100995","pdf_url":"https://www.mdpi.com/1099-4300/27/10/995/pdf?version=1758705160","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3155074394","display_name":null,"funder_award_id":"12090020","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4414477340.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W2027461913","https://openalex.org/W2032402547","https://openalex.org/W2187089797","https://openalex.org/W2221243399","https://openalex.org/W2500751094","https://openalex.org/W2614256707","https://openalex.org/W2789643644","https://openalex.org/W2793272303","https://openalex.org/W2914331134","https://openalex.org/W2942454403","https://openalex.org/W2943270518","https://openalex.org/W3011424859","https://openalex.org/W3103753223","https://openalex.org/W3122028341","https://openalex.org/W3128776197","https://openalex.org/W3170347305","https://openalex.org/W3181729304","https://openalex.org/W4210541032","https://openalex.org/W4212991997","https://openalex.org/W4225146933","https://openalex.org/W4226070402","https://openalex.org/W4285106710","https://openalex.org/W4285187901","https://openalex.org/W4285266400","https://openalex.org/W4291138777","https://openalex.org/W4313229413","https://openalex.org/W4317206949","https://openalex.org/W4322743777","https://openalex.org/W4323313312","https://openalex.org/W4390603854","https://openalex.org/W4390817508","https://openalex.org/W4390948790","https://openalex.org/W4391033652","https://openalex.org/W4392903274","https://openalex.org/W4400238700","https://openalex.org/W4400810737","https://openalex.org/W4404479756","https://openalex.org/W4405778600","https://openalex.org/W4411218326","https://openalex.org/W4412964824","https://openalex.org/W4413147024"],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1],"classification":[2],"(HSIC)":[3],"involves":[4],"analyzing":[5],"high-dimensional":[6],"data":[7],"that":[8,104,166,202],"contain":[9],"substantial":[10],"spectral":[11,107,121,168],"redundancy":[12,27,93,218],"and":[13,20,42,94,109,141,192,220,226],"spatial":[14,174],"noise,":[15],"which":[16,79,137,159],"increases":[17],"the":[18,66,114,143,147],"entropy":[19],"uncertainty":[21],"of":[22,69],"feature":[23,92],"representations.":[24],"Reducing":[25],"such":[26],"while":[28,176],"retaining":[29],"informative":[30],"content":[31,68],"in":[32,146],"spectral-spatial":[33,58,139],"interactions":[34,140],"remains":[35],"a":[36,81,99,119,131,153,161],"fundamental":[37],"challenge":[38],"for":[39,229],"building":[40],"efficient":[41,225],"accurate":[43],"HSIC":[44],"models.":[45,198],"Traditional":[46],"deep":[47],"learning":[48],"methods":[49],"often":[50],"rely":[51],"on":[52,186],"redundant":[53,111],"modules":[54],"or":[55],"lack":[56],"sufficient":[57],"coupling,":[59],"limiting":[60],"their":[61],"ability":[62],"to":[63,90,124,170],"fully":[64],"exploit":[65],"information":[67,145,221],"hyperspectral":[70,190],"data.":[71],"To":[72],"address":[73],"these":[74],"challenges,":[75],"we":[76,97,129,151],"propose":[77],"SGFNet,":[78],"is":[80,160],"spectral-guided":[82],"fusion":[83],"network":[84],"designed":[85,98],"from":[86],"an":[87,212,224],"information-theoretic":[88,213],"perspective":[89],"reduce":[91],"uncertainty.":[95],"First,":[96],"Spectral-Aware":[100],"Filtering":[101],"Module":[102],"(SAFM)":[103],"suppresses":[105],"noisy":[106],"components":[108],"reduces":[110],"entropy,":[112],"encoding":[113],"raw":[115],"pixel-wise":[116],"spectrum":[117],"into":[118],"compact":[120],"representation":[122],"accessible":[123],"all":[125],"encoder":[126],"blocks.":[127],"Second,":[128],"introduced":[130],"Spectral-Spatial":[132],"Adaptive":[133],"Fusion":[134],"(SSAF)":[135],"module,":[136],"strengthens":[138],"enhances":[142],"discriminative":[144],"fused":[148],"features.":[149],"Finally,":[150],"developed":[152],"Spectral":[154],"Guidance":[155],"Gated":[156],"CNN":[157],"(SGGC),":[158],"lightweight":[162],"gated":[163],"convolutional":[164],"module":[165],"uses":[167],"guidance":[169],"more":[171],"effectively":[172],"extract":[173],"representations":[175],"avoiding":[177],"unnecessary":[178],"sequence":[179],"modeling":[180],"overhead.":[181],"We":[182],"conducted":[183],"extensive":[184],"experiments":[185],"four":[187],"widely":[188],"used":[189],"benchmarks":[191],"compared":[193],"SGFNet":[194,203,215],"with":[195],"eight":[196],"state-of-the-art":[197],"The":[199],"results":[200],"demonstrate":[201],"consistently":[204],"achieves":[205],"superior":[206],"performance":[207],"across":[208],"multiple":[209],"metrics.":[210],"From":[211],"perspective,":[214],"implicitly":[216],"balances":[217],"reduction":[219],"preservation,":[222],"providing":[223],"effective":[227],"solution":[228],"HSIC.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
