{"id":"https://openalex.org/W4391406960","doi":"https://doi.org/10.1109/tgrs.2024.3360714","title":"Hyperspectral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders","display_name":"Hyperspectral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391406960","doi":"https://doi.org/10.1109/tgrs.2024.3360714"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3360714","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3360714","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-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":"https://openalex.org/A5101477855","display_name":"Tingting Fang","orcid":"https://orcid.org/0009-0007-9069-7436"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Fang","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0007-9069-7436","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049872589","display_name":"Fei Zhu","orcid":"https://orcid.org/0000-0002-8113-3707"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Zhu","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-8113-3707","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100333004","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0003-2306-8860"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Chen","raw_affiliation_strings":["School of Marine Science and Technology, Northwestern Polytechnical University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-2306-8860","affiliations":[{"raw_affiliation_string":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.2195,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.98178897,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"16"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9922999739646912,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9890000224113464,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multilinear-map","display_name":"Multilinear map","score":0.847699761390686},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8324927091598511},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.778944730758667},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6656708121299744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6014017462730408},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5309897065162659},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5023641586303711},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4601384699344635},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.4482840299606323},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44172123074531555},{"id":"https://openalex.org/keywords/spectral-signature","display_name":"Spectral signature","score":0.4346124529838562},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4145914316177368},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4119194447994232},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.41009676456451416},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34489643573760986},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.341464102268219},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20372021198272705},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.14393824338912964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09120383858680725}],"concepts":[{"id":"https://openalex.org/C84392682","wikidata":"https://www.wikidata.org/wiki/Q1952404","display_name":"Multilinear map","level":2,"score":0.847699761390686},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8324927091598511},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.778944730758667},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6656708121299744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6014017462730408},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5309897065162659},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5023641586303711},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4601384699344635},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.4482840299606323},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44172123074531555},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.4346124529838562},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4145914316177368},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4119194447994232},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.41009676456451416},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34489643573760986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.341464102268219},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20372021198272705},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.14393824338912964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09120383858680725},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"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/tgrs.2024.3360714","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3360714","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6269349810","display_name":null,"funder_award_id":"61701337","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1957094454","https://openalex.org/W1963659868","https://openalex.org/W2005818237","https://openalex.org/W2011315899","https://openalex.org/W2019274094","https://openalex.org/W2078222544","https://openalex.org/W2088259770","https://openalex.org/W2127062304","https://openalex.org/W2127827417","https://openalex.org/W2157321686","https://openalex.org/W2160868783","https://openalex.org/W2163886442","https://openalex.org/W2342048370","https://openalex.org/W2406093411","https://openalex.org/W2774270599","https://openalex.org/W2774517539","https://openalex.org/W2791725003","https://openalex.org/W2792897399","https://openalex.org/W2809306703","https://openalex.org/W2811009023","https://openalex.org/W2894115892","https://openalex.org/W2911419410","https://openalex.org/W2921511952","https://openalex.org/W2953412098","https://openalex.org/W2963069169","https://openalex.org/W2963371848","https://openalex.org/W2974534743","https://openalex.org/W3015126059","https://openalex.org/W3016116861","https://openalex.org/W3028000844","https://openalex.org/W3031843515","https://openalex.org/W3101353736","https://openalex.org/W3110749113","https://openalex.org/W3122463936","https://openalex.org/W3131601043","https://openalex.org/W3137191419","https://openalex.org/W3139578059","https://openalex.org/W3161263451","https://openalex.org/W3165729427","https://openalex.org/W3170878188","https://openalex.org/W3204957802","https://openalex.org/W3205148564","https://openalex.org/W4210555639","https://openalex.org/W4212955958","https://openalex.org/W4281395053","https://openalex.org/W4285173622","https://openalex.org/W4285223668","https://openalex.org/W4289656123","https://openalex.org/W4312917841","https://openalex.org/W4322490806","https://openalex.org/W4383220030","https://openalex.org/W4385958045","https://openalex.org/W6680012447","https://openalex.org/W6746968042"],"related_works":["https://openalex.org/W4318719034","https://openalex.org/W37958683","https://openalex.org/W1999178348","https://openalex.org/W2396820687","https://openalex.org/W4295021234","https://openalex.org/W2806873178","https://openalex.org/W2965146396","https://openalex.org/W2770818364","https://openalex.org/W4404095322","https://openalex.org/W4312416532"],"abstract_inverted_index":{"Unsupervised":[0],"spectral":[1,153],"unmixing":[2,31,55,118],"consists":[3],"of":[4,12,97,175],"representing":[5],"each":[6],"observed":[7],"pixel":[8],"as":[9,17],"a":[10,68,104,112],"combination":[11],"several":[13],"pure":[14],"materials":[15],"known":[16],"endmembers,":[18,137],"along":[19],"with":[20,36],"their":[21],"corresponding":[22],"abundance":[23],"fractions.":[24],"Beyond":[25],"the":[26,37,73,95,131,167,173,176],"linear":[27],"assumption,":[28],"various":[29],"nonlinear":[30,54],"models":[32,130],"have":[33],"been":[34],"proposed,":[35],"associated":[38],"optimization":[39,45],"problems":[40],"solved":[41],"either":[42],"by":[43,71,76],"traditional":[44],"algorithms":[46],"or":[47],"deep":[48,52],"learning":[49],"techniques.":[50],"Current":[51],"learning-based":[53],"mainly":[56],"focuses":[57],"on":[58,120,165],"additive,":[59],"bilinear-based":[60],"formulations.":[61],"The":[62,142,187],"multilinear":[63],"mixing":[64],"model":[65,135],"(MLM)":[66],"offers":[67],"unique":[69],"perspective":[70],"interpreting":[72],"reflection":[74],"process":[75],"discrete":[77],"Markov":[78],"chains,":[79],"allowing":[80],"it":[81],"to":[82,89,183],"account":[83],"for":[84,116],"interactions":[85],"between":[86],"endmembers":[87],"up":[88],"infinite":[90],"order.":[91],"However,":[92],"explicitly":[93,129],"simulating":[94],"physics":[96],"MLM":[98],"using":[99],"neural":[100],"networks":[101],"has":[102],"remained":[103],"challenging":[105],"problem.":[106],"In":[107],"this":[108,127],"paper,":[109],"we":[110],"propose":[111],"novel":[113],"autoencoder-based":[114],"network":[115,125,143],"unsupervised":[117],"based":[119],"MLM.":[121],"Leveraging":[122],"an":[123],"elaborate":[124],"design,":[126],"approach":[128],"relationships":[132],"among":[133],"all":[134],"parameters:":[136],"abundances,":[138],"and":[139,155,169],"transition":[140],"probability.":[141],"operates":[144],"in":[145],"two":[146],"modes:":[147],"MLM-1DAE,":[148],"which":[149,157],"considers":[150],"only":[151],"pixel-wise":[152],"information,":[154],"MLM-3DAE,":[156],"explores":[158],"spectral-spatial":[159],"correlations":[160],"within":[161],"input":[162],"patches.":[163],"Experiments":[164],"both":[166],"synthetic":[168],"real":[170],"datasets":[171],"validate":[172],"effectiveness":[174],"proposed":[177],"method,":[178],"demonstrating":[179],"competitive":[180],"performance":[181],"compared":[182],"classic":[184],"MLM-based":[185],"solutions.":[186],"code":[188],"is":[189],"available":[190],"at":[191],"https://github.com/ting-Fang09/Hyperspectral-unmixing-MLM-AE.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
