{"id":"https://openalex.org/W2242603519","doi":"https://doi.org/10.1109/tgrs.2015.2466657","title":"Integrating Hierarchical Segmentation Maps With MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework","display_name":"Integrating Hierarchical Segmentation Maps With MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework","publication_year":2015,"publication_date":"2015-08-31","ids":{"openalex":"https://openalex.org/W2242603519","doi":"https://doi.org/10.1109/tgrs.2015.2466657","mag":"2242603519"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2015.2466657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2015.2466657","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/A5012334189","display_name":"Meysam Golipour","orcid":null},"institutions":[{"id":"https://openalex.org/I1516879","display_name":"Tarbiat Modares University","ror":"https://ror.org/03mwgfy56","country_code":"IR","type":"education","lineage":["https://openalex.org/I1516879"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Meysam Golipour","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran","Fac. of Electr. & Comput. Eng, Tarbiat Modares Univ., Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]},{"raw_affiliation_string":"Fac. of Electr. & Comput. Eng, Tarbiat Modares Univ., Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040092306","display_name":"Hassan Ghassemian","orcid":"https://orcid.org/0000-0002-2303-1753"},"institutions":[{"id":"https://openalex.org/I1516879","display_name":"Tarbiat Modares University","ror":"https://ror.org/03mwgfy56","country_code":"IR","type":"education","lineage":["https://openalex.org/I1516879"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hassan Ghassemian","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran","Fac. of Electr. & Comput. Eng, Tarbiat Modares Univ., Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]},{"raw_affiliation_string":"Fac. of Electr. & Comput. Eng, Tarbiat Modares Univ., Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040486097","display_name":"Fardin Mirzapour","orcid":"https://orcid.org/0000-0002-0127-472X"},"institutions":[{"id":"https://openalex.org/I1516879","display_name":"Tarbiat Modares University","ror":"https://ror.org/03mwgfy56","country_code":"IR","type":"education","lineage":["https://openalex.org/I1516879"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Fardin Mirzapour","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran","Fac. of Electr. & Comput. Eng, Tarbiat Modares Univ., Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]},{"raw_affiliation_string":"Fac. of Electr. & Comput. Eng, Tarbiat Modares Univ., Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012334189"],"corresponding_institution_ids":["https://openalex.org/I1516879"],"apc_list":null,"apc_paid":null,"fwci":16.5859,"has_fulltext":false,"cited_by_count":86,"citation_normalized_percentile":{"value":0.99202425,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"54","issue":"2","first_page":"805","last_page":"816"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9861000180244446,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7220728993415833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6933652758598328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.626039981842041},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5955111384391785},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.54306560754776},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4991593360900879},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.46138671040534973},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.4446144700050354},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.44447386264801025},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.43575239181518555},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4303678274154663},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.20883983373641968}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7220728993415833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6933652758598328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.626039981842041},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5955111384391785},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.54306560754776},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4991593360900879},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46138671040534973},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.4446144700050354},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.44447386264801025},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.43575239181518555},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4303678274154663},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.20883983373641968},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2015.2466657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2015.2466657","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":[{"id":"https://openalex.org/F4320323298","display_name":"H\u00e1sk\u00f3li \u00cdslands","ror":"https://ror.org/01db6h964"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1485235332","https://openalex.org/W1974524700","https://openalex.org/W1979730959","https://openalex.org/W1998030734","https://openalex.org/W2001298023","https://openalex.org/W2008847349","https://openalex.org/W2017913446","https://openalex.org/W2019338222","https://openalex.org/W2038577255","https://openalex.org/W2043665634","https://openalex.org/W2046373373","https://openalex.org/W2053491764","https://openalex.org/W2062964394","https://openalex.org/W2063385051","https://openalex.org/W2064604707","https://openalex.org/W2098057602","https://openalex.org/W2101309634","https://openalex.org/W2104269704","https://openalex.org/W2105386417","https://openalex.org/W2113137767","https://openalex.org/W2113513024","https://openalex.org/W2114358147","https://openalex.org/W2130463078","https://openalex.org/W2131697388","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2143516773","https://openalex.org/W2147353113","https://openalex.org/W2149471024","https://openalex.org/W2151140083","https://openalex.org/W2152632881","https://openalex.org/W2153635508","https://openalex.org/W2157621128","https://openalex.org/W2158400785","https://openalex.org/W2164437025","https://openalex.org/W2166229804","https://openalex.org/W2166923144","https://openalex.org/W2168481151","https://openalex.org/W4213262319","https://openalex.org/W4241684091","https://openalex.org/W4320339642"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W4233585817","https://openalex.org/W2016045932","https://openalex.org/W1675950995","https://openalex.org/W2188882668","https://openalex.org/W2086619084","https://openalex.org/W2004379491","https://openalex.org/W2083140487","https://openalex.org/W2035045952","https://openalex.org/W2356314591"],"abstract_inverted_index":{"This":[0,33],"paper":[1],"presents":[2],"a":[3,49,54,58],"new":[4],"spectral-spatial":[5],"classification":[6,105,206],"method":[7,14,55,122,167],"for":[8,56],"hyperspectral":[9],"(HS)":[10],"images.":[11],"The":[12,87,160,188],"proposed":[13,88,121,166,194],"is":[15,46,67,168,198],"based":[16],"on":[17],"integrating":[18],"hierarchical":[19,50,195],"segmentation":[20,44,65,196],"results":[21,162],"into":[22],"Markov":[23],"random":[24],"field":[25],"(MRF)":[26],"spatial":[27,79],"prior":[28,80],"in":[29,123,143,205],"the":[30,78,96,117,120,138,165,193,201],"Bayesian":[31,144],"framework.":[32],"work":[34],"includes":[35],"two":[36],"main":[37],"contributions.":[38],"First,":[39],"statistical":[40],"region":[41],"merging":[42],"(SRM)":[43],"algorithm":[45],"extended":[47],"to":[48,76,115,170,176],"version,":[51],"HSRM.":[52],"Second,":[53],"extracting":[57],"multilevel":[59,83],"\u201cfuzzy":[60],"no-border/border\u201d":[61],"map":[62],"from":[63],"HSRM":[64],"hierarchy":[66],"proposed,":[68],"which":[69],"are":[70,109,158],"then":[71],"exploited":[72],"as":[73,91,183,185],"weighting":[74],"coefficients":[75],"modify":[77],"of":[81,99,104,119,192],"MRF-based":[82,100,177],"logistic":[84,154],"(MLL)":[85],"model.":[86],"method,":[89],"named":[90],"MRF":[92,132],"+":[93],"HSRM,":[94],"addresses":[95],"common":[97],"problem":[98],"methods,":[101,178],"i.e.,":[102],"over-smoothing":[103],"result.":[106],"Several":[107],"experiments":[108],"conducted":[110],"using":[111],"real":[112],"HS":[113],"images":[114],"evaluate":[116],"performance":[118],"comparison":[124],"with":[125],"conventional":[126],"MRF,":[127],"and":[128,133,151],"some":[129],"state-of-the-art":[130],"weighted":[131],"object-based":[134],"classifiers.":[135],"To":[136],"estimate":[137],"class":[139,181],"conditional":[140],"probability":[141],"distribution":[142],"framework,":[145],"probabilistic":[146],"support":[147],"vector":[148],"machine":[149],"(SVM)":[150],"subspace":[152],"multinomial":[153],"regression":[155],"(MLRsub)":[156],"classifiers":[157],"used.":[159],"experimental":[161],"demonstrate":[163],"that":[164],"able":[169],"generate":[171],"more":[172],"homogeneous":[173],"regions":[174],"similar":[175],"while":[179],"preserving":[180],"boundaries":[182],"accurately":[184],"segmentation-based":[186],"methods.":[187],"overhead":[189],"computational":[190],"burden":[191],"stage":[197],"negligible":[199],"considering":[200],"improvement":[202],"it":[203],"offers":[204],"results.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":16},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}