{"id":"https://openalex.org/W7147425093","doi":"https://doi.org/10.1109/cw68232.2025.00036","title":"Graph-Enhanced Spatial-Spectral U-Net for Hyperspectral Image Classification","display_name":"Graph-Enhanced Spatial-Spectral U-Net for Hyperspectral Image Classification","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7147425093","doi":"https://doi.org/10.1109/cw68232.2025.00036"},"language":null,"primary_location":{"id":"doi:10.1109/cw68232.2025.00036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cw68232.2025.00036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Cyberworlds (CW\uff09","raw_type":"proceedings-article"},"type":"conference-paper","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/A5132597052","display_name":"Yage Song","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yage Song","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132559770","display_name":"Aoyuan Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]},{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aoyuan Shi","raw_affiliation_strings":["School of Artificial Intelligence, Taiyuan University of Technology,Taiyuan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Taiyuan University of Technology,Taiyuan,China","institution_ids":["https://openalex.org/I46305995","https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087354586","display_name":"Xueying Liu","orcid":"https://orcid.org/0009-0009-0874-7970"},"institutions":[{"id":"https://openalex.org/I46482218","display_name":"Anqing Normal University","ror":"https://ror.org/0127ytz78","country_code":"CN","type":"education","lineage":["https://openalex.org/I46482218"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueying Liu","raw_affiliation_strings":["School of Computer and Information, Anqing Normal University,Anqing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anqing Normal University,Anqing,China","institution_ids":["https://openalex.org/I46482218"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132634765","display_name":"Jie Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Zhou","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79621721,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9312000274658203,"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":0.9312000274658203,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.015300000086426735,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.006800000090152025,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6189000010490417},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5551999807357788},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5491999983787537},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46639999747276306},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4560000002384186},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.44850000739097595},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4438999891281128},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.36550000309944153},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.35830000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833999752998352},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6189000010490417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.558899998664856},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5491999983787537},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46639999747276306},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4560000002384186},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.44850000739097595},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.35830000042915344},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2913999855518341},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26460000872612},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C194995250","wikidata":"https://www.wikidata.org/wiki/Q531136","display_name":"Affordance","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C110893760","wikidata":"https://www.wikidata.org/wiki/Q3115590","display_name":"Named graph","level":5,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cw68232.2025.00036","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cw68232.2025.00036","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Cyberworlds (CW\uff09","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1901129140","https://openalex.org/W1966580635","https://openalex.org/W2039409148","https://openalex.org/W2109255472","https://openalex.org/W2476548250","https://openalex.org/W2500751094","https://openalex.org/W2572303978","https://openalex.org/W2772452219","https://openalex.org/W2884436604","https://openalex.org/W2892621946","https://openalex.org/W2914331134","https://openalex.org/W2942454403","https://openalex.org/W2963881378","https://openalex.org/W2964015378","https://openalex.org/W2991494819","https://openalex.org/W2991616716","https://openalex.org/W3047443805","https://openalex.org/W3095675139","https://openalex.org/W3200705513","https://openalex.org/W3214821343","https://openalex.org/W4225146933","https://openalex.org/W4226334005","https://openalex.org/W4280571954","https://openalex.org/W4313131622","https://openalex.org/W4365141265","https://openalex.org/W4379469137","https://openalex.org/W4386602105","https://openalex.org/W4387259421","https://openalex.org/W4393119650","https://openalex.org/W4408499345"],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1,46],"(HSI)":[2],"classification":[3],"demands":[4],"the":[5,114,166],"sophisticated":[6],"modeling":[7],"of":[8,168],"both":[9],"intricate":[10],"spatial":[11,103],"contexts":[12],"and":[13,48,52,65,134,149],"long-range":[14],"spectral":[15,30,120],"dependencies.":[16],"Prevailing":[17],"methods,":[18,164],"however,":[19],"struggle":[20,61],"with":[21,62],"a":[22,79,93,106,136],"fundamental":[23],"trade-off:":[24],"Convolutional":[25],"Neural":[26,39],"Networks":[27,40],"(CNNs)":[28],"exhibit":[29],"myopia":[31],"due":[32],"to":[33,125],"their":[34,69],"local":[35],"receptive":[36],"fields;":[37],"Graph":[38,108],"(GNNs)":[41],"applied":[42],"spatially":[43],"can":[44],"disrupt":[45],"integrity":[47],"incur":[49],"prohibitive":[50],"costs;":[51],"Transformers,":[53],"while":[54],"adept":[55],"at":[56,113],"capturing":[57],"global":[58,128],"dependencies,":[59],"often":[60],"quadratic":[63],"complexity":[64],"data-hungry":[66],"nature,":[67],"limiting":[68],"scalability":[70],"for":[71,83],"HSI":[72],"tasks.":[73],"To":[74],"resolve":[75],"this,":[76],"we":[77],"propose$\\mathrm{S}^{2}$G-UNet,":[78],"unified":[80],"framework":[81],"architected":[82],"synergistic":[84,170],"spatial-spectral":[85],"feature":[86],"learning.$\\mathrm{S}^{2}$G-UNet":[87],"introduces":[88],"three":[89,155],"key":[90],"innovations:":[91],"(1)":[92],"Hierarchical":[94],"Hybrid":[95],"Feature":[96],"Encoder":[97],"(HHFE)":[98],"that":[99,141],"captures":[100],"rich,":[101],"multi-scale":[102],"features;":[104],"(2)":[105],"Spectral":[107],"Refinement":[109],"Module":[110,139],"(SGRM),":[111],"positioned":[112],"network":[115],"bottleneck,":[116],"which":[117],"uniquely":[118],"models":[119],"bands":[121],"as":[122],"graph":[123,132],"nodes":[124],"explicitly":[126],"capture":[127],"inter-band":[129],"correlations":[130],"via":[131],"attention;":[133],"(3)":[135],"Feature-Guided":[137],"Interaction":[138],"(FGIM)":[140],"ensures":[142],"robust":[143],"bidirectional":[144],"semantic":[145],"alignment":[146],"between":[147],"encoder":[148],"decoder":[150],"pathways.":[151],"Extensive":[152],"experiments":[153],"on":[154],"benchmark":[156],"datasets":[157],"demonstrate":[158],"that$\\mathrm{S}^{2}":[159],"\\mathrm{G}$-UNet":[160],"substantially":[161],"outperforms":[162],"contemporary":[163],"validating":[165],"efficacy":[167],"its":[169],"design.":[171]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-02T00:00:00"}