{"id":"https://openalex.org/W7118018670","doi":"https://doi.org/10.48550/arxiv.2512.24172","title":"Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges","display_name":"Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges","publication_year":2025,"publication_date":"2025-12-30","ids":{"openalex":"https://openalex.org/W7118018670","doi":"https://doi.org/10.48550/arxiv.2512.24172"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.24172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24172","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.24172","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121755038","display_name":"Yu-Tang Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Yu-Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121821691","display_name":"Pin-Wei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Pin-Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121767693","display_name":"Shih-Fang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Shih-Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9329000115394592,"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":0.9329000115394592,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.007499999832361937,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.006899999920278788,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7099999785423279},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6880999803543091},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5436000227928162},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.491100013256073},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44690001010894775},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.385699987411499},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3702000081539154},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.36419999599456787},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35740000009536743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746999979019165},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7099999785423279},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6880999803543091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6251999735832214},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5436000227928162},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.491100013256073},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44690001010894775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.385699987411499},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.36419999599456787},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.34299999475479126},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.24172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24172","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.24172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24172","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6980800628662109,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"imaging":[1],"(HSI)":[2],"analysis":[3],"faces":[4],"computational":[5],"bottlenecks":[6],"due":[7,140],"to":[8,32,34,84,141,166],"massive":[9],"data":[10,172],"volumes":[11],"that":[12,65],"exceed":[13],"available":[14,174],"memory.":[15],"While":[16],"foundation":[17],"models":[18],"pre-trained":[19],"on":[20,78,93],"large":[21],"remote":[22],"sensing":[23],"datasets":[24],"show":[25],"promise,":[26],"their":[27],"learned":[28],"representations":[29,135],"often":[30],"fail":[31],"transfer":[33],"domain-specific":[35],"applications":[36],"like":[37],"close-range":[38],"agricultural":[39],"monitoring":[40],"where":[41],"spectral":[42],"signatures,":[43],"spatial":[44],"scales,":[45],"and":[46,113,171],"semantic":[47,120],"targets":[48],"differ":[49],"fundamentally.":[50],"This":[51],"report":[52],"presents":[53],"Deep":[54],"Global":[55],"Clustering":[56],"(DGC),":[57],"a":[58,102],"conceptual":[59],"framework":[60,124],"for":[61],"memory-efficient":[62],"HSI":[63],"segmentation":[64],"learns":[66],"global":[67],"clustering":[68],"structure":[69],"from":[70,126],"local":[71],"patch":[72],"observations":[73],"without":[74],"pre-training.":[75],"DGC":[76,106],"operates":[77],"small":[79],"patches":[80],"with":[81],"overlapping":[82],"regions":[83],"enforce":[85],"consistency,":[86],"enabling":[87],"training":[88],"in":[89,130,144],"under":[90],"30":[91],"minutes":[92],"consumer":[94],"hardware":[95],"while":[96],"maintaining":[97],"constant":[98],"memory":[99],"usage.":[100],"On":[101],"leaf":[103],"disease":[104,116],"dataset,":[105],"achieves":[107],"background-tissue":[108],"separation":[109],"(mean":[110],"IoU":[111],"0.925)":[112],"demonstrates":[114],"unsupervised":[115],"detection":[117],"through":[118],"navigable":[119],"granularity.":[121],"However,":[122],"the":[123,155],"suffers":[125],"optimization":[127],"instability":[128],"rooted":[129],"multi-objective":[131],"loss":[132,168],"balancing:":[133],"meaningful":[134],"emerge":[136],"rapidly":[137],"but":[138,160],"degrade":[139],"cluster":[142],"over-merging":[143],"feature":[145],"space.":[146],"We":[147],"position":[148],"this":[149],"work":[150],"as":[151],"intellectual":[152],"scaffolding":[153],"-":[154],"design":[156],"philosophy":[157],"has":[158],"merit,":[159],"stable":[161],"implementation":[162],"requires":[163],"principled":[164],"approaches":[165],"dynamic":[167],"balancing.":[169],"Code":[170],"are":[173],"at":[175],"https://github.com/b05611038/HSI_global_clustering.":[176]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-02T00:00:00"}