{"id":"https://openalex.org/W2984176366","doi":"https://doi.org/10.1109/igarss.2019.8898060","title":"Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model","display_name":"Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2984176366","doi":"https://doi.org/10.1109/igarss.2019.8898060","mag":"2984176366"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8898060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5026292374","display_name":"Alessandro Montaldo","orcid":null},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessandro Montaldo","raw_affiliation_strings":["DITEN dept., University of Genoa, Genoa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DITEN dept., University of Genoa, Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020314840","display_name":"Luca Fronda","orcid":null},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Fronda","raw_affiliation_strings":["DITEN dept., University of Genoa, Genoa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DITEN dept., University of Genoa, Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009677880","display_name":"Ihsen Hedhli","orcid":"https://orcid.org/0000-0001-9452-3221"},"institutions":[{"id":"https://openalex.org/I43406934","display_name":"Universit\u00e9 Laval","ror":"https://ror.org/04sjchr03","country_code":"CA","type":"education","lineage":["https://openalex.org/I43406934"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ihsen Hedhli","raw_affiliation_strings":["Laval University, Quebec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laval University, Quebec, Canada","institution_ids":["https://openalex.org/I43406934"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012936131","display_name":"Gabriele Moser","orcid":"https://orcid.org/0000-0002-3796-2938"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Gabriele Moser","raw_affiliation_strings":["DITEN dept., University of Genoa, Genoa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DITEN dept., University of Genoa, Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018158883","display_name":"Josiane Zerubia","orcid":"https://orcid.org/0000-0002-7444-0856"},"institutions":[{"id":"https://openalex.org/I4210106545","display_name":"Centre Inria d'Universit\u00e9 C\u00f4te d'Azur","ror":"https://ror.org/01nzkaw91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I201841394","https://openalex.org/I4210106545"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Josiane Zerubia","raw_affiliation_strings":["Inria, UCA, Sophia-Antipolis M\u00e9diterran\u00e9e Center, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inria, UCA, Sophia-Antipolis M\u00e9diterran\u00e9e Center, France","institution_ids":["https://openalex.org/I4210106545"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041360967","display_name":"Sebastiano B. Serpico","orcid":"https://orcid.org/0000-0001-9858-7230"},"institutions":[{"id":"https://openalex.org/I83816512","display_name":"University of Genoa","ror":"https://ror.org/0107c5v14","country_code":"IT","type":"education","lineage":["https://openalex.org/I83816512"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sebastiano B. Serpico","raw_affiliation_strings":["DITEN dept., University of Genoa, Genoa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DITEN dept., University of Genoa, Genoa, Italy","institution_ids":["https://openalex.org/I83816512"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.175,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77395833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2810","last_page":"2813"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9811000227928162,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9717000126838684,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7103593349456787},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.6751445531845093},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.6281037926673889},{"id":"https://openalex.org/keywords/multiresolution-analysis","display_name":"Multiresolution analysis","score":0.5481522083282471},{"id":"https://openalex.org/keywords/quadtree","display_name":"Quadtree","score":0.531523585319519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49777939915657043},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.48010018467903137},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.47029945254325867},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4345487058162689},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4247887432575226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4231290817260742},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4153144657611847},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4145650565624237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41076529026031494},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3598940968513489},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.3247912526130676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29796087741851807},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.18732252717018127},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.18060261011123657},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14975187182426453},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08494073152542114}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7103593349456787},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.6751445531845093},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.6281037926673889},{"id":"https://openalex.org/C121927907","wikidata":"https://www.wikidata.org/wiki/Q1952516","display_name":"Multiresolution analysis","level":5,"score":0.5481522083282471},{"id":"https://openalex.org/C151416825","wikidata":"https://www.wikidata.org/wiki/Q934791","display_name":"Quadtree","level":2,"score":0.531523585319519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49777939915657043},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.48010018467903137},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.47029945254325867},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4345487058162689},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4247887432575226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4231290817260742},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4153144657611847},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4145650565624237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41076529026031494},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3598940968513489},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3247912526130676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29796087741851807},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.18732252717018127},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.18060261011123657},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14975187182426453},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08494073152542114},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/igarss.2019.8898060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898060","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-02157082v1","is_oa":false,"landing_page_url":"https://inria.hal.science/hal-02157082","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":"IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:iris.unige.it:11567/1012755","is_oa":false,"landing_page_url":"http://hdl.handle.net/11567/1012755","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W137456267","https://openalex.org/W1615149086","https://openalex.org/W1651266332","https://openalex.org/W1678356000","https://openalex.org/W1971478845","https://openalex.org/W2056132907","https://openalex.org/W2064903336","https://openalex.org/W2150757437","https://openalex.org/W2162021123","https://openalex.org/W2412925410","https://openalex.org/W2516739718","https://openalex.org/W2612918980","https://openalex.org/W2768533672","https://openalex.org/W2911964244","https://openalex.org/W4213262319"],"related_works":["https://openalex.org/W987019958","https://openalex.org/W2030382593","https://openalex.org/W1983122994","https://openalex.org/W2078379274","https://openalex.org/W4233015508","https://openalex.org/W2099971354","https://openalex.org/W2133071149","https://openalex.org/W2101965184","https://openalex.org/W2574864235","https://openalex.org/W2166485063"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"the":[3,6,45,90],"problem":[4],"of":[5,8,111],"classification":[7],"multiresolution":[9],"and":[10,25,51,66],"multisensor":[11,26],"remotely":[12],"sensed":[13],"data":[14,81],"is":[15],"addressed":[16],"by":[17,83],"proposing":[18],"a":[19,40,52],"multiscale":[20],"Markov":[21,54],"mesh":[22,55],"model.":[23],"Multiresolution":[24],"fusion":[27],"are":[28,86],"jointly":[29],"achieved":[30],"through":[31,88],"an":[32],"explicitly":[33],"hierarchical":[34,77],"probabilistic":[35],"graphical":[36],"classifier,":[37],"which":[38,118],"uses":[39],"quadtree":[41],"structure":[42],"to":[43,58,69,122],"model":[44,107],"interactions":[46],"across":[47],"different":[48],"spatial":[49],"resolutions,":[50],"symmetric":[53],"random":[56],"field":[57],"deal":[59],"with":[60],"contextual":[61],"information":[62],"at":[63],"each":[64,103],"scale":[65],"favor":[67],"applicability":[68],"very":[70],"high":[71],"resolution":[72],"imagery.":[73],"Differently":[74],"from":[75],"previous":[76],"Markovian":[78],"approaches,":[79],"here,":[80],"collected":[82],"distinct":[84],"sensors":[85],"fused":[87],"either":[89],"graph":[91],"topology":[92],"itself":[93],"(across":[94],"its":[95],"layers)":[96],"or":[97],"decision":[98],"tree":[99],"ensemble":[100],"methods":[101],"(within":[102],"layer).":[104],"The":[105],"proposed":[106],"allows":[108],"taking":[109],"benefit":[110],"strong":[112],"analytical":[113],"properties,":[114],"most":[115],"remarkably":[116],"causality,":[117],"make":[119],"it":[120],"possible":[121],"apply":[123],"time-efficient":[124],"non-iterative":[125],"inference":[126],"algorithms.":[127]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}