{"id":"https://openalex.org/W4387389647","doi":"https://doi.org/10.1109/tci.2023.3321985","title":"Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing","display_name":"Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387389647","doi":"https://doi.org/10.1109/tci.2023.3321985"},"language":"en","primary_location":{"id":"doi:10.1109/tci.2023.3321985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2023.3321985","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2573-0436"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2310.02340","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028555319","display_name":"Ricardo Augusto Borsoi","orcid":"https://orcid.org/0000-0001-6036-2124"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210110792","display_name":"Centre de Recherche en Automatique de Nancy","ror":"https://ror.org/022r5hc56","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210099203","https://openalex.org/I4210100260","https://openalex.org/I4210110792","https://openalex.org/I4210159245","https://openalex.org/I90183372"]},{"id":"https://openalex.org/I90183372","display_name":"Universit\u00e9 de Lorraine","ror":"https://ror.org/04vfs2w97","country_code":"FR","type":"education","lineage":["https://openalex.org/I90183372"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Ricardo Augusto Borsoi","raw_affiliation_strings":["Université de Lorraine, CNRS, CRAN, Vandoeuvre-lès-Nancy, France"],"affiliations":[{"raw_affiliation_string":"Université de Lorraine, CNRS, CRAN, Vandoeuvre-lès-Nancy, France","institution_ids":["https://openalex.org/I4210110792","https://openalex.org/I90183372","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083261801","display_name":"Deniz Erdo\u011fmu\u015f","orcid":"https://orcid.org/0000-0002-1114-3539"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]},{"id":"https://openalex.org/I905180023","display_name":"Eastern University","ror":"https://ror.org/04txhk134","country_code":"US","type":"education","lineage":["https://openalex.org/I905180023"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deniz Erdo\u011fmu\u015f","raw_affiliation_strings":["ECE department of the Northeastern University, Boston, MA, USA","ECE department of the North-eastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"ECE department of the Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"ECE department of the North-eastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I905180023","https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009495271","display_name":"Tales Imbiriba","orcid":"https://orcid.org/0000-0002-2626-2039"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]},{"id":"https://openalex.org/I905180023","display_name":"Eastern University","ror":"https://ror.org/04txhk134","country_code":"US","type":"education","lineage":["https://openalex.org/I905180023"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tales Imbiriba","raw_affiliation_strings":["ECE department of the Northeastern University, Boston, MA, USA","ECE department of the North-eastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"ECE department of the Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"ECE department of the North-eastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I905180023","https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028555319"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210110792","https://openalex.org/I90183372"],"apc_list":null,"apc_paid":null,"fwci":1.7425,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86728838,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"977","last_page":"991"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9987999796867371,"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":0.9900000095367432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.8770144581794739},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8261584043502808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6990752816200256},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6663655042648315},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6481767296791077},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6211031675338745},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5084149241447449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4973655045032501},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.42440569400787354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42145836353302},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.4181510806083679}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.8770144581794739},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8261584043502808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6990752816200256},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6663655042648315},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6481767296791077},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6211031675338745},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5084149241447449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4973655045032501},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.42440569400787354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42145836353302},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.4181510806083679}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tci.2023.3321985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2023.3321985","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2573-0436"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2310.02340","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.02340","pdf_url":"https://arxiv.org/pdf/2310.02340","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-04247144v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04247144","pdf_url":"https://hal.science/hal-04247144v1/file/2310.02340.pdf","source":{"id":"https://openalex.org/S4406922466","display_name":"SPIRE - Sciences Po Institutional REpository","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Computational Imaging, 2023, 9, pp.977-991. ⟨10.1109/TCI.2023.3321985⟩","raw_type":"Journal articles"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2310.02340","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.02340","pdf_url":"https://arxiv.org/pdf/2310.02340","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G592642646","display_name":null,"funder_award_id":"NGS-86713T-21","funder_id":"https://openalex.org/F4320309024","funder_display_name":"National Geographic Society"}],"funders":[{"id":"https://openalex.org/F4320309024","display_name":"National Geographic Society","ror":"https://ror.org/04bqh5m06"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387389647.pdf"},"referenced_works_count":129,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1524571335","https://openalex.org/W1590183771","https://openalex.org/W1601795611","https://openalex.org/W1646152356","https://openalex.org/W1669655639","https://openalex.org/W1959608418","https://openalex.org/W1977824599","https://openalex.org/W2003896345","https://openalex.org/W2005818237","https://openalex.org/W2008535112","https://openalex.org/W2011315899","https://openalex.org/W2019274094","https://openalex.org/W2032944446","https://openalex.org/W2037328426","https://openalex.org/W2039039627","https://openalex.org/W2042626896","https://openalex.org/W2060147006","https://openalex.org/W2066612219","https://openalex.org/W2081867871","https://openalex.org/W2084724634","https://openalex.org/W2087263574","https://openalex.org/W2107219953","https://openalex.org/W2108501770","https://openalex.org/W2113898416","https://openalex.org/W2121955539","https://openalex.org/W2124781164","https://openalex.org/W2125298866","https://openalex.org/W2127495569","https://openalex.org/W2128462828","https://openalex.org/W2133782497","https://openalex.org/W2139047226","https://openalex.org/W2143500192","https://openalex.org/W2144881411","https://openalex.org/W2157321686","https://openalex.org/W2159444483","https://openalex.org/W2331832821","https://openalex.org/W2490662969","https://openalex.org/W2555818261","https://openalex.org/W2566771666","https://openalex.org/W2621357189","https://openalex.org/W2755992512","https://openalex.org/W2760901445","https://openalex.org/W2765455392","https://openalex.org/W2773194520","https://openalex.org/W2791725003","https://openalex.org/W2792897399","https://openalex.org/W2804275394","https://openalex.org/W2804766086","https://openalex.org/W2885173122","https://openalex.org/W2894115892","https://openalex.org/W2903420725","https://openalex.org/W2910655660","https://openalex.org/W2911419410","https://openalex.org/W2913942978","https://openalex.org/W2921511952","https://openalex.org/W2942765877","https://openalex.org/W2948978827","https://openalex.org/W2950821630","https://openalex.org/W2952478253","https://openalex.org/W2953412098","https://openalex.org/W2963095307","https://openalex.org/W2963128373","https://openalex.org/W2963371848","https://openalex.org/W2964144928","https://openalex.org/W2970126910","https://openalex.org/W2981731882","https://openalex.org/W2991135466","https://openalex.org/W3002092414","https://openalex.org/W3012359476","https://openalex.org/W3015126059","https://openalex.org/W3028000844","https://openalex.org/W3031843515","https://openalex.org/W3044978601","https://openalex.org/W3047443805","https://openalex.org/W3092495663","https://openalex.org/W3096435923","https://openalex.org/W3097353710","https://openalex.org/W3100234884","https://openalex.org/W3100619029","https://openalex.org/W3101012758","https://openalex.org/W3103695279","https://openalex.org/W3106244377","https://openalex.org/W3106289623","https://openalex.org/W3122463936","https://openalex.org/W3130365209","https://openalex.org/W3131601043","https://openalex.org/W3133902371","https://openalex.org/W3133902755","https://openalex.org/W3137191419","https://openalex.org/W3140885850","https://openalex.org/W3151666947","https://openalex.org/W3154512708","https://openalex.org/W3154628895","https://openalex.org/W3157105957","https://openalex.org/W3161263451","https://openalex.org/W3164711602","https://openalex.org/W3165729427","https://openalex.org/W3189122062","https://openalex.org/W3192188223","https://openalex.org/W3198761571","https://openalex.org/W3200993454","https://openalex.org/W3204957802","https://openalex.org/W3205148564","https://openalex.org/W3213181738","https://openalex.org/W3214821343","https://openalex.org/W4200095805","https://openalex.org/W4205095826","https://openalex.org/W4206155389","https://openalex.org/W4206259850","https://openalex.org/W4206620949","https://openalex.org/W4233760599","https://openalex.org/W4287178529","https://openalex.org/W4297692234","https://openalex.org/W4309573499","https://openalex.org/W4366082848","https://openalex.org/W4372184044","https://openalex.org/W4392677642","https://openalex.org/W6631190155","https://openalex.org/W6636774829","https://openalex.org/W6640963894","https://openalex.org/W6675944832","https://openalex.org/W6683562536","https://openalex.org/W6712840351","https://openalex.org/W6734688555","https://openalex.org/W6738835615","https://openalex.org/W6739922093","https://openalex.org/W6752110675","https://openalex.org/W6762120954"],"related_works":["https://openalex.org/W2037328426","https://openalex.org/W1990914742","https://openalex.org/W3106536224","https://openalex.org/W2891033441","https://openalex.org/W2006559622","https://openalex.org/W2315521504","https://openalex.org/W2890371384","https://openalex.org/W2773863718","https://openalex.org/W2563324120","https://openalex.org/W2040756827"],"abstract_inverted_index":{"Although":[0],"considerable":[1],"effort":[2],"has":[3],"been":[4,44],"dedicated":[5],"to":[6,10,37,50,114,148,197],"improving":[7],"the":[8,11,26,64,67,117,143,158,164,190,193],"solution":[9],"hyperspectral":[12,47,91],"unmixing":[13,68,92],"problem,":[14,69],"non-idealities":[15,65],"such":[16,58],"as":[17,160],"complex":[18],"radiation":[19],"scattering":[20],"and":[21,32,53,97,119,129,172,186],"endmem-ber":[22],"variability":[23],"negatively":[24],"impact":[25],"performance":[27,191],"of":[28,66,153,178,192],"most":[29],"existing":[30],"algorithms":[31],"can":[33],"be":[34],"very":[35],"challenging":[36],"address.":[38],"Recently,":[39],"deep":[40,87,168],"learning-based":[41],"frameworks":[42],"have":[43],"explored":[45],"for":[46,90,95],"umixing":[48],"due":[49],"their":[51],"flexibility":[52],"powerful":[54],"representation":[55],"capabilities.":[56],"However,":[57],"techniques":[59],"either":[60],"do":[61],"not":[62,77],"address":[63],"or":[70],"rely":[71],"on":[72,184],"black-box":[73],"models":[74],"which":[75,135],"are":[76],"interpretable.":[78],"In":[79],"this":[80],"paper,":[81],"we":[82],"propose":[83],"a":[84,104,132,150,161],"new":[85],"interpretable":[86],"learning":[88,111,140],"method":[89,102,195],"that":[93],"accounts":[94],"nonlinearity":[96],"endmember":[98],"variability.":[99],"The":[100,121],"proposed":[101,194],"leverages":[103,136],"probabilistic":[105],"variational":[106],"deep-learning":[107],"framework,":[108],"where":[109],"disentanglement":[110],"is":[112,123,145],"employed":[113],"properly":[115],"separate":[116],"abundances":[118,159],"endmembers.":[120],"model":[122,144],"learned":[124],"end-to-end":[125],"using":[126,131,166,173],"stochastic":[127],"backpropagation,":[128],"trained":[130],"self-supervised":[133],"strategy":[134],"benefits":[137],"from":[138],"semi-supervised":[139],"techniques.":[141],"Furthermore,":[142],"carefully":[146],"designed":[147],"provide":[149],"high":[151],"degree":[152],"interpretability.":[154],"This":[155],"includes":[156],"modeling":[157],"Dirichlet":[162],"distribution,":[163],"endmembers":[165],"low-dimensional":[167],"latent":[169],"variable":[170],"representations,":[171],"two-stream":[174],"neural":[175],"networks":[176],"composed":[177],"additive":[179],"piecewise-linear/nonlinear":[180],"components.":[181],"Experimental":[182],"results":[183],"synthetic":[185],"real":[187],"datasets":[188],"illustrate":[189],"compared":[196],"state-of-the-art":[198],"algorithms.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2023-10-06T00:00:00"}