{"id":"https://openalex.org/W3005360455","doi":"https://doi.org/10.1109/lgrs.2020.2967104","title":"Unsupervised Bayesian Subpixel Mapping of Hyperspectral Imagery Based on Band-Weighted Discrete Spectral Mixture Model and Markov Random Field","display_name":"Unsupervised Bayesian Subpixel Mapping of Hyperspectral Imagery Based on Band-Weighted Discrete Spectral Mixture Model and Markov Random Field","publication_year":2020,"publication_date":"2020-02-04","ids":{"openalex":"https://openalex.org/W3005360455","doi":"https://doi.org/10.1109/lgrs.2020.2967104","mag":"3005360455"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.2967104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.2967104","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5087355805","display_name":"Yujia Chen","orcid":"https://orcid.org/0000-0002-2510-6333"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujia Chen","raw_affiliation_strings":["School of Land Science and Technology, China University of Geosciences Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2510-6333","affiliations":[{"raw_affiliation_string":"School of Land Science and Technology, China University of Geosciences Beijing, Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034166335","display_name":"Linlin Xu","orcid":"https://orcid.org/0000-0002-3488-5199"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Linlin Xu","raw_affiliation_strings":["University of Waterloo Faculty of Engineering, Waterloo, Canada"],"raw_orcid":"https://orcid.org/0000-0002-3488-5199","affiliations":[{"raw_affiliation_string":"University of Waterloo Faculty of Engineering, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuan Fang","orcid":"https://orcid.org/0000-0003-4401-6517"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yuan Fang","raw_affiliation_strings":["University of Waterloo Faculty of Engineering, Waterloo, Canada"],"raw_orcid":"https://orcid.org/0000-0003-4401-6517","affiliations":[{"raw_affiliation_string":"University of Waterloo Faculty of Engineering, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026394989","display_name":"Junhuan Peng","orcid":"https://orcid.org/0000-0002-0587-9486"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junhuan Peng","raw_affiliation_strings":["School of Land Science and Technology, China University of Geosciences Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Land Science and Technology, China University of Geosciences Beijing, Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103535034","display_name":"Wenfu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenfu Yang","raw_affiliation_strings":["Shanxi Key Laboratory of Resources, Environment and Disaster Monitoring, Jinzhong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi Key Laboratory of Resources, Environment and Disaster Monitoring, Jinzhong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102920751","display_name":"Alexander Wong","orcid":"https://orcid.org/0000-0001-5729-5899"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alexander Wong","raw_affiliation_strings":["University of Waterloo Faculty of Engineering, Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo Faculty of Engineering, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066149942","display_name":"David A. Clausi","orcid":"https://orcid.org/0000-0002-6383-0875"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"David A. Clausi","raw_affiliation_strings":["University of Waterloo Faculty of Engineering, Waterloo, Canada"],"raw_orcid":"https://orcid.org/0000-0002-6383-0875","affiliations":[{"raw_affiliation_string":"University of Waterloo Faculty of Engineering, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8908,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88794073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"18","issue":"1","first_page":"162","last_page":"166"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9950000047683716,"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":0.9812999963760376,"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/endmember","display_name":"Endmember","score":0.9290920495986938},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8958081603050232},{"id":"https://openalex.org/keywords/subpixel-rendering","display_name":"Subpixel rendering","score":0.7814604043960571},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.7355284690856934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6591392755508423},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6461508274078369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6143474578857422},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6100009679794312},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4992854595184326},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.49257713556289673},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4144245684146881},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.3623013496398926},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28096747398376465},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15550941228866577},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1144399344921112},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.10814431309700012}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.9290920495986938},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8958081603050232},{"id":"https://openalex.org/C68516990","wikidata":"https://www.wikidata.org/wiki/Q452912","display_name":"Subpixel rendering","level":3,"score":0.7814604043960571},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.7355284690856934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6591392755508423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6461508274078369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6143474578857422},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6100009679794312},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4992854595184326},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.49257713556289673},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4144245684146881},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3623013496398926},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28096747398376465},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15550941228866577},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1144399344921112},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.10814431309700012},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.2967104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.2967104","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2574134513","display_name":"\u591a\u5e74\u51bb\u571f\u70ed\u529b\u7a33\u5b9a\u6027\u5bf9\u6c14\u5019-\u751f\u6001\u73af\u5883-\u5de5\u7a0b\u6d3b\u52a8\u7684\u590d\u5408\u54cd\u5e94\u8fc7\u7a0b\u548c\u673a\u7406","funder_award_id":"41330634","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3292258137","display_name":"\u968f\u673a\u4fe1\u53f7\u975e\u5e73\u7a33\u60c5\u51b5\u4e0b\u7684\u7a7a\u95f4\u6ee4\u6ce2\u4e0e\u63a8\u4f30\u65b9\u6cd5\u7814\u7a76\u53ca\u5176\u5e94\u7528","funder_award_id":"41374016","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7089208991","display_name":"\u57fa\u4e8e\u6df7\u6c8c\u8bef\u5dee\u7406\u8bba\u7684GPS\u7535\u79bb\u5c42\u7efc\u5408\u5206\u6790\u4e0e\u9884\u62a5\u7814\u7a76","funder_award_id":"41104025","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"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1970687769","https://openalex.org/W1976843198","https://openalex.org/W1991018729","https://openalex.org/W1994348192","https://openalex.org/W2026999441","https://openalex.org/W2048147566","https://openalex.org/W2081155276","https://openalex.org/W2157321686","https://openalex.org/W2315100696","https://openalex.org/W2558732717","https://openalex.org/W4253856589"],"related_works":["https://openalex.org/W2394258921","https://openalex.org/W2772759470","https://openalex.org/W2903571773","https://openalex.org/W1997259586","https://openalex.org/W4288063198","https://openalex.org/W2170079321","https://openalex.org/W987019958","https://openalex.org/W4233015508","https://openalex.org/W2030382593","https://openalex.org/W1983122994"],"abstract_inverted_index":{"Although":[0],"accurate":[1],"training":[2],"and":[3,48,72,94,113,128,141,145,161,169],"initialization":[4],"information":[5,20,74,143],"is":[6,21,63,150],"difficult":[7],"to":[8,136,152],"acquire,":[9],"unsupervised":[10,35,65],"hyperspectral":[11,38],"subpixel":[12],"mapping":[13],"(SPM)":[14],"without":[15],"relying":[16,78],"on":[17,42,79,166],"this":[18,62,83],"predefined":[19],"an":[22,64,146],"insufficiently":[23],"addressed":[24],"research":[25],"issue.":[26],"This":[27],"letter":[28],"presents":[29],"a":[30,49,133],"novel":[31],"Bayesian":[32,134],"approach":[33,66,84,149],"for":[34,76,89],"SPM":[36,107,116],"of":[37,70,86,99],"imagery":[39],"(HSI)":[40],"based":[41],"the":[43,57,87,91,95,109,120,126,139,154,159,162,174],"Markov":[44],"random":[45],"field":[46,98],"(MRF)":[47],"band-weighted":[50],"discrete":[51],"spectral":[52,110,142],"mixture":[53,111],"model":[54,155],"(BDSMM),":[55],"with":[56],"following":[58],"key":[59],"characteristics.":[60],"First,":[61],"that":[67,173],"allows":[68,114],"adjustment":[69],"abundance":[71],"endmember":[73],"adaptively":[75],"less":[77],"algorithm":[80,176],"initialization.":[81],"Second,":[82],"consists":[85],"BDSMM":[88,104,129],"accommodating":[90],"noise":[92],"heterogeneity":[93],"hidden":[96],"label":[97,163],"subpixels":[100],"in":[101,123],"HSI.":[102,124],"The":[103],"also":[105],"integrates":[106],"into":[108,132],"analysis":[112],"enhanced":[115],"by":[117,156],"fully":[118],"exploring":[119],"endmember-abundance":[121],"patterns":[122],"Third,":[125],"MRF":[127],"are":[130],"integrated":[131],"framework":[135],"use":[137],"both":[138,167],"spatial":[140],"efficiently,":[144],"expectation-maximization":[147],"(EM)":[148],"designed":[151],"solve":[153],"iteratively":[157],"estimating":[158],"endmembers":[160],"field.":[164],"Experiments":[165],"simulated":[168],"real":[170],"HSI":[171],"demonstrate":[172],"proposed":[175],"can":[177],"yield":[178],"better":[179],"performance":[180],"than":[181],"traditional":[182],"methods.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
