{"id":"https://openalex.org/W4407737392","doi":"https://doi.org/10.1109/whispers65427.2024.10876482","title":"Learning By Evolving Nonlinear Diffusion for Active Learning on Hyperspectral Images","display_name":"Learning By Evolving Nonlinear Diffusion for Active Learning on Hyperspectral Images","publication_year":2024,"publication_date":"2024-12-09","ids":{"openalex":"https://openalex.org/W4407737392","doi":"https://doi.org/10.1109/whispers65427.2024.10876482"},"language":"en","primary_location":{"id":"doi:10.1109/whispers65427.2024.10876482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers65427.2024.10876482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-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":null,"display_name":"Kabir Tripathi","orcid":null},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kabir Tripathi","raw_affiliation_strings":["Tufts University,Department of Mathematics,Medford,MA,USA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Mathematics,Medford,MA,USA,02155","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022923838","display_name":"James M. Murphy","orcid":"https://orcid.org/0000-0001-6598-044X"},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James M. Murphy","raw_affiliation_strings":["Tufts University,Department of Mathematics,Medford,MA,USA,02155"],"affiliations":[{"raw_affiliation_string":"Tufts University,Department of Mathematics,Medford,MA,USA,02155","institution_ids":["https://openalex.org/I121934306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I121934306"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3948774,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.6308000087738037,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.6308000087738037,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.6273999810218811,"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/T13493","display_name":"Optical and Acousto-Optic Technologies","score":0.6129000186920166,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8802605867385864},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6342787742614746},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.6108046770095825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5231360793113708},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.47372522950172424},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4115902781486511},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3269980251789093},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3265964686870575},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.19302120804786682},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14735102653503418}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8802605867385864},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6342787742614746},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.6108046770095825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5231360793113708},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.47372522950172424},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4115902781486511},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3269980251789093},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3265964686870575},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.19302120804786682},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14735102653503418},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers65427.2024.10876482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers65427.2024.10876482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4665180110","display_name":null,"funder_award_id":"2309519,2318894","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190"],"abstract_inverted_index":{"A":[0,147],"method":[1],"for":[2],"active":[3],"learning":[4],"of":[5,65,118,154],"hyperspectral":[6],"images":[7],"(HSI)":[8],"is":[9,35,45,139],"proposed,":[10],"which":[11,41,135],"leverages":[12],"time-evolving":[13],"diffusion":[14,33,78,96,119],"processes":[15],"on":[16,37,144],"graphs":[17],"to":[18,26,74,122,131],"determine":[19],"query":[20],"points":[21,84],"and":[22,152],"propagate":[23],"their":[24],"labels":[25,71],"the":[27,54,59,66,76,90,95,115,136,145,150],"full":[28],"data":[29],"set.":[30],"Initially,":[31],"a":[32,38,46,132],"process":[34],"defined":[36],"graph":[39],"in":[40,58,85,94,134],"each":[42,63],"HSI":[43,148],"pixel":[44],"node,":[47],"with":[48,53,98,114,126],"edge":[49],"connection":[50],"strength":[51],"scaling":[52],"distance":[55],"between":[56,83],"pixels":[57],"spectral":[60],"domain.":[61],"At":[62],"stage":[64],"iterative":[67],"sampling":[68],"process,":[69],"queried":[70],"are":[72],"used":[73],"update":[75],"underlying":[77,137],"matrix":[79,97,138],"by":[80,106],"weakening":[81],"edges":[82],"different":[86],"classes,":[87],"thereby":[88],"infusing":[89],"intrinsic":[91],"representation":[92],"encoded":[93],"revealed":[99],"label":[100],"information.":[101],"The":[102],"proposed":[103],"method,":[104],"Learning":[105],"Evolving":[107],"Nonlinear":[108],"Diffusion":[109],"(LEND),":[110],"combines":[111],"robust":[112],"performance":[113],"mathematical":[116],"tractability":[117],"geometry,":[120],"leading":[121],"superior":[123],"labeling":[124],"accuracy":[125],"fewer":[127],"labeled":[128],"samples":[129],"compared":[130],"baseline":[133],"not":[140],"iteratively":[141],"updated.":[142],"Experiments":[143],"Salinas":[146],"demonstrate":[149],"effectiveness":[151],"efficiency":[153],"LEND.":[155]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}