{"id":"https://openalex.org/W2029557055","doi":"https://doi.org/10.1109/tgrs.2012.2217380","title":"Multipixel Retrieval of Structural and Optical Parameters in a 2-D Scene With a Path-Recycling Monte Carlo Forward Model and a New Bayesian Inference Engine","display_name":"Multipixel Retrieval of Structural and Optical Parameters in a 2-D Scene With a Path-Recycling Monte Carlo Forward Model and a New Bayesian Inference Engine","publication_year":2012,"publication_date":"2012-11-20","ids":{"openalex":"https://openalex.org/W2029557055","doi":"https://doi.org/10.1109/tgrs.2012.2217380","mag":"2029557055"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2012.2217380","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2217380","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/A5002908234","display_name":"Ian Langmore","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ian Langmore","raw_affiliation_strings":["Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA","[Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"[Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA]","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032214093","display_name":"Anthony B. Davis","orcid":"https://orcid.org/0000-0003-1279-1420"},"institutions":[{"id":"https://openalex.org/I1334627681","display_name":"Jet Propulsion Laboratory","ror":"https://ror.org/027k65916","country_code":"US","type":"facility","lineage":["https://openalex.org/I122411786","https://openalex.org/I1334627681","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony B. Davis","raw_affiliation_strings":["Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA","Jet Propulsion Laboratory , California Institute of Technology , Pasadena , CA , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA","institution_ids":["https://openalex.org/I1334627681"]},{"raw_affiliation_string":"Jet Propulsion Laboratory , California Institute of Technology , Pasadena , CA , USA","institution_ids":["https://openalex.org/I1334627681"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060627660","display_name":"Guillaume Bal","orcid":"https://orcid.org/0000-0002-4174-647X"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guillaume Bal","raw_affiliation_strings":["Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA","[Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"[Department of Applied Physics & Applied Mathematics, Columbia University\u2019s, New York, NY, USA]","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4588,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.69188737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"51","issue":"5","first_page":"2903","last_page":"2919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10347","display_name":"Atmospheric aerosols and clouds","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10347","display_name":"Atmospheric aerosols and clouds","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11320","display_name":"Atmospheric Ozone and Climate","score":0.9983000159263611,"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/monte-carlo-method","display_name":"Monte Carlo method","score":0.6405743360519409},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6390424966812134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5755961537361145},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5000765323638916},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.4984774589538574},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.43534350395202637},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43374207615852356},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.37014180421829224},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.33288389444351196},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2619202733039856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25801151990890503},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.24730601906776428},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14861005544662476},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10636588931083679}],"concepts":[{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6405743360519409},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6390424966812134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5755961537361145},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5000765323638916},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.4984774589538574},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.43534350395202637},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43374207615852356},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37014180421829224},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.33288389444351196},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2619202733039856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25801151990890503},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.24730601906776428},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14861005544662476},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10636588931083679},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2012.2217380","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2217380","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":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1130514660","display_name":null,"funder_award_id":"DMS-0804696","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":55,"referenced_works":["https://openalex.org/W33902070","https://openalex.org/W201299100","https://openalex.org/W650099020","https://openalex.org/W1592316977","https://openalex.org/W1617570115","https://openalex.org/W1649460031","https://openalex.org/W1883798832","https://openalex.org/W1965950155","https://openalex.org/W1970914933","https://openalex.org/W1976171325","https://openalex.org/W1978971115","https://openalex.org/W1985093013","https://openalex.org/W1985575764","https://openalex.org/W1985882281","https://openalex.org/W1993332713","https://openalex.org/W1997958688","https://openalex.org/W1999182952","https://openalex.org/W2001947605","https://openalex.org/W2011837434","https://openalex.org/W2012755947","https://openalex.org/W2020994040","https://openalex.org/W2042770989","https://openalex.org/W2056760934","https://openalex.org/W2060827275","https://openalex.org/W2063261639","https://openalex.org/W2065787591","https://openalex.org/W2066860228","https://openalex.org/W2067722023","https://openalex.org/W2077512328","https://openalex.org/W2078449547","https://openalex.org/W2081287315","https://openalex.org/W2084043696","https://openalex.org/W2087972079","https://openalex.org/W2089413135","https://openalex.org/W2093979814","https://openalex.org/W2098065523","https://openalex.org/W2108386319","https://openalex.org/W2118587791","https://openalex.org/W2132134745","https://openalex.org/W2134213291","https://openalex.org/W2138309709","https://openalex.org/W2142033032","https://openalex.org/W2147120321","https://openalex.org/W2149662495","https://openalex.org/W2150601115","https://openalex.org/W2152498489","https://openalex.org/W2159875796","https://openalex.org/W2162311884","https://openalex.org/W2238050544","https://openalex.org/W2315651246","https://openalex.org/W2322398337","https://openalex.org/W3135227555","https://openalex.org/W4210924444","https://openalex.org/W4292691288","https://openalex.org/W4388375774"],"related_works":["https://openalex.org/W2385371209","https://openalex.org/W2076134148","https://openalex.org/W1991437568","https://openalex.org/W2067727414","https://openalex.org/W2083270190","https://openalex.org/W2998323711","https://openalex.org/W2948825694","https://openalex.org/W1992306031","https://openalex.org/W4299569200","https://openalex.org/W2039637073"],"abstract_inverted_index":{"Physics-based":[0],"retrievals":[1],"of":[2,116,133,146,164,185,209,230,234,238],"atmosphere":[3],"and/or":[4],"surface":[5],"properties":[6,225],"are":[7,62],"generally":[8],"multi-":[9],"or":[10],"hyperspectral":[11],"in":[12,102,139,143,200],"nature;":[13],"some":[14],"use":[15,97,170],"multi-angle":[16],"information":[17],"as":[18],"well.":[19],"Recently,":[20],"polarization":[21],"has":[22],"been":[23],"added":[24,246],"to":[25,68,84,93,111,127,157,198,206,216],"the":[26,73,82,94,114,144,154,177,223,227,235],"available":[27],"input":[28],"from":[29],"sensors":[30],"and":[31,108,131,219,226,240],"accordingly":[32],"modeled":[33],"with":[34],"vector":[35],"radiative":[36],"transfer":[37],"(RT).":[38],"At":[39],"any":[40],"rate,":[41],"a":[42,48,51,98,117,140,171,201],"single":[43],"pixel":[44],"is":[45,125,180,195,214,245],"processed":[46],"at":[47],"time":[49],"using":[50,188],"forward":[52,99,159,193],"RT":[53,100,210],"model":[54,101,194,211],"predicated":[55],"on":[56,72,76],"1-D":[57],"transport":[58],"theory.":[59],"Neighboring":[60],"pixels":[61,239],"sometimes":[63],"considered":[64],"but,":[65],"generally,":[66],"just":[67],"formulate":[69],"statistical":[70],"constraints":[71],"inversion":[74,204],"based":[75],"spatial":[77],"context.":[78],"Herein,":[79],"we":[80,169],"demonstrate":[81],"power":[83],"be":[85],"harnessed":[86],"by":[87,187],"adding":[88],"bona":[89],"fide":[90],"multipixel":[91],"techniques":[92],"mix.":[95],"We":[96,151],"2-D,":[103],"sufficient":[104],"for":[105,113,167,248],"this":[106,207],"demonstration":[107],"easily":[109],"extended":[110],"3-D,":[112],"response":[115],"single-wavelength":[118],"imaging":[119],"sensor.":[120],"The":[121],"data,":[122],"an":[123,134],"image,":[124],"used":[126],"infer":[128],"position,":[129],"size,":[130],"opacity":[132],"absorbing":[135],"atmospheric":[136],"plume":[137,224],"somewhere":[138],"deep":[141],"valley":[142],"presence":[145],"partially":[147],"known/partially":[148],"unknown":[149],"aerosol.":[150,231],"first":[152],"describe":[153],"necessary":[155],"innovation":[156],"speed-up":[158],"multidimensional":[160],"RT.":[161],"In":[162,232],"spite":[163,233],"its":[165],"reputation":[166],"inefficiency,":[168],"Monte":[172],"Carlo":[173],"(MC)":[174],"technique.":[175],"However,":[176],"adopted":[178],"scheme":[179],"highly":[181],"accelerated":[182],"without":[183],"loss":[184],"accuracy":[186],"\u201crecycled\u201d":[189],"MC":[190],"paths.":[191],"This":[192],"then":[196],"put":[197],"work":[199],"novel":[202],"Bayesian":[203],"adapted":[205],"kind":[208],"where":[212],"it":[213],"straightforward":[215],"trade":[217],"precision":[218],"efficiency.":[220],"Retrievals":[221],"target":[222],"specific":[228],"amount":[229],"limited":[236],"number":[237],"low":[241],"signal-to-noise":[242],"ratio,":[243],"there":[244],"value":[247],"certain":[249],"nuclear":[250],"treaty":[251],"verification":[252],"applications.":[253]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
