{"id":"https://openalex.org/W4385757761","doi":"https://doi.org/10.1109/tgrs.2023.3304484","title":"MAHUM: A Multitasks Autoencoder Hyperspectral Unmixing Model","display_name":"MAHUM: A Multitasks Autoencoder Hyperspectral Unmixing Model","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4385757761","doi":"https://doi.org/10.1109/tgrs.2023.3304484"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3304484","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3304484","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/A5010242765","display_name":"Jia Chen","orcid":"https://orcid.org/0000-0002-9896-1656"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Jia Chen","raw_affiliation_strings":["Department of Electrical, Biomedical, and Computer Engineering, University of Pavia, Pavia, Italy"],"raw_orcid":"https://orcid.org/0000-0002-9896-1656","affiliations":[{"raw_affiliation_string":"Department of Electrical, Biomedical, and Computer Engineering, University of Pavia, Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006623289","display_name":"Paolo Gamba","orcid":"https://orcid.org/0000-0002-9576-6337"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Gamba","raw_affiliation_strings":["Department of Electrical, Biomedical, and Computer Engineering, University of Pavia, Pavia, Italy"],"raw_orcid":"https://orcid.org/0000-0002-9576-6337","affiliations":[{"raw_affiliation_string":"Department of Electrical, Biomedical, and Computer Engineering, University of Pavia, Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100362041","display_name":"Jun Li","orcid":"https://orcid.org/0000-0003-1613-9448"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-1613-9448","affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4378,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84110253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"61","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":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/T13890","display_name":"Remote Sensing and Land Use","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.954066812992096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7921959161758423},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.771774411201477},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7276170253753662},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6872340440750122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6695760488510132},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6362952589988708},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5934783220291138},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5711700916290283},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.4800232946872711},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.330223947763443},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32462313771247864},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2701341211795807},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1239844262599945}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.954066812992096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921959161758423},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.771774411201477},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7276170253753662},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6872340440750122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6695760488510132},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6362952589988708},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5934783220291138},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5711700916290283},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.4800232946872711},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.330223947763443},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32462313771247864},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2701341211795807},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1239844262599945},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3304484","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3304484","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1755724306","https://openalex.org/W1902016676","https://openalex.org/W2019149505","https://openalex.org/W2032944446","https://openalex.org/W2042626896","https://openalex.org/W2051358843","https://openalex.org/W2094596373","https://openalex.org/W2101837437","https://openalex.org/W2114486983","https://openalex.org/W2127062304","https://openalex.org/W2141494774","https://openalex.org/W2142786738","https://openalex.org/W2143500192","https://openalex.org/W2157321686","https://openalex.org/W2161865324","https://openalex.org/W2163886442","https://openalex.org/W2169924573","https://openalex.org/W2527329788","https://openalex.org/W2765455392","https://openalex.org/W2768479014","https://openalex.org/W2774528199","https://openalex.org/W2809306703","https://openalex.org/W2893348249","https://openalex.org/W2894115892","https://openalex.org/W2911419410","https://openalex.org/W2921511952","https://openalex.org/W2932657337","https://openalex.org/W2935130647","https://openalex.org/W3028000844","https://openalex.org/W3035015656","https://openalex.org/W3041330594","https://openalex.org/W3101195009","https://openalex.org/W3110749113","https://openalex.org/W3131601043","https://openalex.org/W3161263451","https://openalex.org/W3168931281","https://openalex.org/W3204957802","https://openalex.org/W4205095826","https://openalex.org/W4206690402","https://openalex.org/W4212955958","https://openalex.org/W4233760599","https://openalex.org/W4285171615","https://openalex.org/W4306399448","https://openalex.org/W4313438466","https://openalex.org/W4366150256","https://openalex.org/W4366310757","https://openalex.org/W4376481071","https://openalex.org/W4380478936"],"related_works":["https://openalex.org/W4287995534","https://openalex.org/W3129266902","https://openalex.org/W2998168123","https://openalex.org/W3105255022","https://openalex.org/W1992306031","https://openalex.org/W2541791370","https://openalex.org/W3034655717","https://openalex.org/W2897995864","https://openalex.org/W2292254049","https://openalex.org/W2035976912"],"abstract_inverted_index":{"Hyperspectral":[0,115],"unmixing":[1,29,91],"is":[2],"a":[3,39,53],"crucial":[4],"task":[5],"in":[6,66,153],"hyperspectral":[7,40],"image":[8,41],"processing":[9],"and":[10,22,80,138],"analysis.":[11],"It":[12],"aims":[13],"to":[14,75,82,122,146],"decompose":[15],"mixed":[16],"pixels":[17,84],"into":[18],"pure":[19],"spectral":[20],"signatures":[21],"their":[23],"associated":[24],"abundances.":[25],"However,":[26],"most":[27],"current":[28],"methods":[30,155],"ignore":[31],"the":[32,35,63,87,106,148],"reality":[33],"that":[34],"same":[36],"pixel":[37],"of":[38,89,101,127,150],"has":[42],"many":[43],"different":[44,83,151,154],"reflections":[45,126],"simultaneously.":[46],"To":[47],"address":[48],"this":[49],"issue,":[50],"we":[51],"propose":[52],"multi-task":[54],"autoencoding":[55],"model":[56],"for":[57],"multiple":[58,120],"reflections,":[59],"which":[60,103,118],"can":[61,96],"improve":[62,105],"algorithm\u2019s":[64,107],"robustness":[65],"complex":[67,128],"environments.":[68],"Our":[69],"proposed":[70,94],"framework":[71],"uses":[72],"3D-CNN-based":[73],"networks":[74],"jointly":[76],"learn":[77],"spectral-spatial":[78],"priors":[79],"adapt":[81],"by":[85],"complementing":[86],"advantages":[88],"other":[90],"methods.":[92],"The":[93],"method":[95],"quantitatively":[97],"evaluate":[98],"each":[99],"area":[100],"data,":[102],"helps":[104],"interpretability.":[108],"This":[109],"paper":[110],"presents":[111],"MAHUM":[112],"(Multi-tasks":[113],"Autoencoder":[114],"Unmixing":[116],"Model),":[117],"stacks":[119],"models":[121],"deal":[123],"with":[124],"various":[125],"terrain.":[129],"We":[130],"also":[131],"perform":[132],"sensitivity":[133],"analysis":[134],"on":[135],"some":[136],"parameters":[137],"show":[139],"experimental":[140],"results":[141],"demonstrating":[142],"our":[143],"method\u2019s":[144],"ability":[145],"express":[147],"adaptability":[149],"materials":[152],"quantitatively.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
