{"id":"https://openalex.org/W4312501973","doi":"https://doi.org/10.1109/tgrs.2022.3211554","title":"Retrieval of Physical Parameters With Deep Structured Kernel Regression","display_name":"Retrieval of Physical Parameters With Deep Structured Kernel Regression","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312501973","doi":"https://doi.org/10.1109/tgrs.2022.3211554"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3211554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3211554","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/A5039052506","display_name":"Gustau Camps\u2010Valls","orcid":"https://orcid.org/0000-0003-1683-2138"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gustau Camps-Valls","raw_affiliation_strings":["Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain"],"raw_orcid":"https://orcid.org/0000-0003-1683-2138","affiliations":[{"raw_affiliation_string":"Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047808140","display_name":"Manuel Campos\u2010Taberner","orcid":"https://orcid.org/0000-0001-5929-3942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manuel Campos-Taberner","raw_affiliation_strings":["Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain"],"raw_orcid":"https://orcid.org/0000-0001-5929-3942","affiliations":[{"raw_affiliation_string":"Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065281659","display_name":"Valero Laparra","orcid":"https://orcid.org/0000-0001-7531-9890"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valero Laparra","raw_affiliation_strings":["Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain"],"raw_orcid":"https://orcid.org/0000-0001-7531-9890","affiliations":[{"raw_affiliation_string":"Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016290792","display_name":"Luca Martino","orcid":"https://orcid.org/0000-0002-7611-6558"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luca Martino","raw_affiliation_strings":["Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain"],"raw_orcid":"https://orcid.org/0000-0002-7611-6558","affiliations":[{"raw_affiliation_string":"Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085480844","display_name":"Jordi Mu\u00f1oz-Mar\u0131\u0301","orcid":"https://orcid.org/0000-0002-3014-3921"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jordi Munoz-Mari","raw_affiliation_strings":["Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain"],"raw_orcid":"https://orcid.org/0000-0002-3014-3921","affiliations":[{"raw_affiliation_string":"Image Processing Laboratory (IPL), Universitat de València, Paterna, València, Spain","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2775,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.64386214,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9154999852180481,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9154999852180481,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6140382885932922},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5565161108970642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4976394474506378},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4796786308288574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3819131553173065},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3300539553165436},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2725967764854431},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21030566096305847},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18031474947929382}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140382885932922},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5565161108970642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4976394474506378},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4796786308288574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3819131553173065},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3300539553165436},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2725967764854431},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21030566096305847},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18031474947929382},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2022.3211554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3211554","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"},{"id":"pmh:oai:HAL:hal-05599285v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05599285","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"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 Geoscience and Remote Sensing, 2022, 60 (4709110), pp.1-10. ⟨10.1109/TGRS.2022.3211554⟩","raw_type":"Journal articles"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5883926154","display_name":"Statistical Learning for Earth Observation Data Analysis.","funder_award_id":"647423","funder_id":"https://openalex.org/F4320338335","funder_display_name":"H2020 European Research Council"}],"funders":[{"id":"https://openalex.org/F4320338335","display_name":"H2020 European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W59495185","https://openalex.org/W631638564","https://openalex.org/W1510073064","https://openalex.org/W1515020792","https://openalex.org/W1595738034","https://openalex.org/W1907195750","https://openalex.org/W1964357740","https://openalex.org/W1970664916","https://openalex.org/W1978239093","https://openalex.org/W1980881904","https://openalex.org/W1987607942","https://openalex.org/W2007342648","https://openalex.org/W2032394154","https://openalex.org/W2052256290","https://openalex.org/W2071190035","https://openalex.org/W2077336029","https://openalex.org/W2115647273","https://openalex.org/W2118955589","https://openalex.org/W2124225314","https://openalex.org/W2139211176","https://openalex.org/W2140095548","https://openalex.org/W2141407058","https://openalex.org/W2145539952","https://openalex.org/W2145544165","https://openalex.org/W2152366160","https://openalex.org/W2162421262","https://openalex.org/W2167881994","https://openalex.org/W2170938446","https://openalex.org/W2413379912","https://openalex.org/W2482630804","https://openalex.org/W2592385957","https://openalex.org/W2755091472","https://openalex.org/W2790936515","https://openalex.org/W3082871349","https://openalex.org/W3087464462","https://openalex.org/W3144619878","https://openalex.org/W3183975070","https://openalex.org/W3216660278","https://openalex.org/W4206733017","https://openalex.org/W4211049957","https://openalex.org/W4211171471","https://openalex.org/W4252656291","https://openalex.org/W4376463272","https://openalex.org/W6608896378","https://openalex.org/W6675357706","https://openalex.org/W6678356451","https://openalex.org/W6681474176","https://openalex.org/W6683084963","https://openalex.org/W6684770997","https://openalex.org/W6684918892","https://openalex.org/W6686319040","https://openalex.org/W6729403857","https://openalex.org/W6747679287","https://openalex.org/W6749314701","https://openalex.org/W6753263680","https://openalex.org/W6754253499"],"related_works":["https://openalex.org/W2110459882","https://openalex.org/W2151022383","https://openalex.org/W2103444992","https://openalex.org/W2141018987","https://openalex.org/W2100852738","https://openalex.org/W2118043379","https://openalex.org/W2076520961","https://openalex.org/W2603933437","https://openalex.org/W2049539567","https://openalex.org/W2348964713"],"abstract_inverted_index":{"Retrieval":[0],"of":[1,5,57,83,92,122,163,197],"physical":[2,110],"parameters":[3,232],"is":[4,152,181],"paramount":[6],"relevance":[7],"for":[8,109],"Earth":[9],"monitoring.":[10],"Statistical":[11],"(machine)":[12],"learning":[13],"approaches":[14],"have":[15,45,76],"been":[16],"successfully":[17],"introduced":[18],"in":[19,64,81,155,221],"the":[20,58,84,90,93,105,119,129,161,195,218,251],"community,":[21],"because":[22],"they":[23,49],"can":[24,61],"learn":[25],"nonlinear":[26,54,78],"functional":[27],"relations":[28],"from":[29,243,258],"observational":[30],"data":[31],"with":[32,118],"no":[33],"strong":[34,77],"a":[35,100,134,141,171,185,213],"priori":[36],"assumptions":[37],"and":[38,71,80,125,144,190,201,224,238,250,255],"parametric":[39],"forms.":[40],"However,":[41],"these":[42],"methods":[43],"still":[44],"two":[46,106],"relevant":[47],"problems:":[48],"only":[50,52],"consider":[51],"one":[53],"feature":[55,166],"map":[56],"data,":[59],"which":[60],"be":[62],"limiting":[63],"complex":[65],"problems":[66,108,229],"where":[67],"inputs":[68],"(e.g.,":[69,73],"radiances)":[70],"outputs":[72],"state":[74],"vectors)":[75],"relations,":[79],"most":[82],"cases,":[85],"models":[86],"do":[87],"not":[88],"incorporate":[89],"structure":[91,132,210],"output":[94,130,209],"(dependent)":[95],"variables.":[96],"This":[97],"article":[98],"proposes":[99],"kernel":[101,147,158,165,175],"method":[102,139,180],"that":[103,206],"solves":[104],"aforementioned":[107],"parameter":[111,227],"retrieval:":[112],"first,":[113],"it":[114,127],"performs":[115],"multioutput":[116],"regression":[117,177],"desired":[120],"number":[121],"connected":[123],"mappings,":[124,167],"second,":[126],"incorporates":[128],"variables":[131],"via":[133],"dedicated":[135],"kernel.":[136],"The":[137,179],"proposed":[138],"has":[140],"closed-form":[142],"solution,":[143],"thus,":[145],"neither":[146],"dimensionality":[148],"reduction":[149],"nor":[150],"preimaging":[151],"necessary":[153],"unlike":[154],"previous":[156],"structured":[157,174],"methods.":[159],"Through":[160],"definition":[162],"appropriate":[164],"we":[168],"also":[169],"derive":[170],"pragmatic":[172],"deep":[173],"ridge":[176],"(KRR).":[178],"characterized":[182],"statistically":[183],"using":[184],"Gaussian":[186],"process":[187],"(GP)":[188],"treatment":[189],"providing":[191],"guarantees":[192],"based":[193],"on":[194],"concepts":[196],"leverage":[198],"scores":[199],"(LSs)":[200],"effective":[202],"dimension:":[203],"both":[204],"explain":[205],"including":[207],"an":[208],"acts":[211],"as":[212],"powerful":[214],"regularizer.":[215],"We":[216],"illustrate":[217],"method\u2019s":[219],"performance":[220],"toy":[222],"examples":[223],"remote":[225],"sensing":[226],"estimation":[228],"involving":[230],"vegetation":[231,240],"[chlorophyll,":[233],"leaf":[234],"area":[235],"index":[236],"(LAI),":[237],"fractional":[239],"cover":[241],"(FVC)]":[242],"compact":[244],"high-resolution":[245],"imaging":[246],"spectrometer":[247],"(CHRIS)":[248],"images":[249],"atmospheric":[252,260],"temperature,":[253],"moisture,":[254],"ozone":[256],"profiles":[257],"infrared":[259],"sounding":[261],"interferometer":[262],"(IASI)":[263],"data.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}