{"id":"https://openalex.org/W2402742998","doi":"https://doi.org/10.1109/tgrs.2016.2562018","title":"A Probabilistic Framework for Spectral\u2013Spatial Classification of Hyperspectral Images","display_name":"A Probabilistic Framework for Spectral\u2013Spatial Classification of Hyperspectral Images","publication_year":2016,"publication_date":"2016-05-22","ids":{"openalex":"https://openalex.org/W2402742998","doi":"https://doi.org/10.1109/tgrs.2016.2562018","mag":"2402742998"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2016.2562018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2562018","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/A5100654056","display_name":"Jinlin Liu","orcid":"https://orcid.org/0000-0002-9761-5831"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinlin Liu","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9761-5831","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011509629","display_name":"Wenkai Lu","orcid":"https://orcid.org/0000-0003-0249-2144"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenkai Lu","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100654056"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.6369,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.86896668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"54","issue":"9","first_page":"5375","last_page":"5384"},"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":0.9944999814033508,"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":0.9430999755859375,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8413808345794678},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7101962566375732},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6893041729927063},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6704668998718262},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6409366130828857},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6082580089569092},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5929089784622192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5636652708053589},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5281503200531006},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4605601727962494},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4274356961250305},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.31483012437820435},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.160002201795578},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09173339605331421}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8413808345794678},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7101962566375732},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6893041729927063},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6704668998718262},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6409366130828857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6082580089569092},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5929089784622192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5636652708053589},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5281503200531006},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4605601727962494},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4274356961250305},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.31483012437820435},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.160002201795578},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09173339605331421}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2016.2562018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2562018","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":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W59495185","https://openalex.org/W1503398984","https://openalex.org/W1532610010","https://openalex.org/W1571150687","https://openalex.org/W1971180200","https://openalex.org/W1982753270","https://openalex.org/W2001298023","https://openalex.org/W2008847349","https://openalex.org/W2020321855","https://openalex.org/W2020999234","https://openalex.org/W2033849769","https://openalex.org/W2059279601","https://openalex.org/W2082880010","https://openalex.org/W2084724634","https://openalex.org/W2092071303","https://openalex.org/W2095190520","https://openalex.org/W2097250277","https://openalex.org/W2101711129","https://openalex.org/W2105386417","https://openalex.org/W2107966405","https://openalex.org/W2108597246","https://openalex.org/W2112981956","https://openalex.org/W2113513024","https://openalex.org/W2114256843","https://openalex.org/W2114819256","https://openalex.org/W2121399909","https://openalex.org/W2122177361","https://openalex.org/W2124571274","https://openalex.org/W2124839057","https://openalex.org/W2126517171","https://openalex.org/W2136251662","https://openalex.org/W2145837601","https://openalex.org/W2148791530","https://openalex.org/W2149471024","https://openalex.org/W2153635508","https://openalex.org/W2156316030","https://openalex.org/W2160662337","https://openalex.org/W2161672030","https://openalex.org/W2163994077","https://openalex.org/W2164437025","https://openalex.org/W2166012115","https://openalex.org/W2166658286","https://openalex.org/W2478493250","https://openalex.org/W2501746074","https://openalex.org/W2535656608","https://openalex.org/W2911964244","https://openalex.org/W3106194068","https://openalex.org/W4214564766","https://openalex.org/W4230674625","https://openalex.org/W4242714600","https://openalex.org/W4248253651","https://openalex.org/W6681497849","https://openalex.org/W6786033154","https://openalex.org/W6824798764"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2889302474","https://openalex.org/W2005234362","https://openalex.org/W2162970382","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Classification":[0],"of":[1,17,40,49,79],"hyperspectral":[2],"images":[3],"usually":[4],"suffers":[5],"from":[6,75,116],"high":[7,42],"dimensionality":[8],"and":[9,29,45,71,85,211,229,238,243],"few":[10],"reference":[11,164],"data,":[12],"which":[13,24,66,96],"limits":[14],"the":[15,18,26,30,34,41,46,50,68,72,76,82,86,93,98,117,121,147,158,162,180,202,205],"performance":[16],"pixelwise":[19,125,242],"classifiers.":[20,221,245],"The":[21,108,232],"spectral-spatial":[22,63,133,144,220,244],"classifiers,":[23],"integrate":[25,138],"spectral":[27,69,83],"data":[28,70,84,165],"spatial":[31,73,87,139],"information":[32,74,88,140],"during":[33],"classification,":[35],"perform":[36],"impressively":[37],"in":[38,146,152,183],"terms":[39],"classification":[43,51,64,109],"accuracy":[44],"homogeneous":[47],"appearance":[48],"map.":[52],"In":[53,120,169,187,201,222],"this":[54,188],"paper,":[55],"we":[56,224],"propose":[57],"a":[58,124,132],"new":[59],"probabilistic":[60,77,126,206],"framework":[61,149],"for":[62],"(PFSSC),":[65],"integrates":[67],"point":[78],"view.":[80],"Both":[81],"are":[89,150,176,192,215],"used":[90],"to":[91,104,156,166,194,217],"estimate":[92],"per-pixel":[94],"probability,":[95],"gives":[97],"likelihood":[99],"that":[100,236],"one":[101,105,134],"pixel":[102],"belongs":[103],"class,":[106],"respectively.":[107,231],"map":[110],"can":[111,128,137],"then":[112],"be":[113,129,218],"directly":[114],"derived":[115],"joint":[118],"probability.":[119],"proposed":[122,148,203],"framework,":[123,204],"classifier":[127],"extended":[130,216],"as":[131,227],"since":[135],"it":[136],"easily.":[141],"Furthermore,":[142],"these":[143],"classifiers":[145],"realized":[151],"an":[153],"iterative":[154,171,185,189],"way":[155],"avoid":[157],"problem":[159],"caused":[160],"by":[161,178,198],"limited":[163],"some":[167,173,241],"extent.":[168],"each":[170],"step,":[172],"unassigned":[174],"pixels":[175,181,191],"classified":[177],"considering":[179],"assigned":[182,193],"previous":[184],"steps.":[186],"process,":[190],"specific":[195],"labels":[196],"step":[197,199],"gradually.":[200],"support":[207],"vector":[208],"machine":[209],"(SVM)":[210],"random":[212],"forest":[213],"(RF)":[214],"two":[219],"short,":[223],"denote":[225],"them":[226],"SVM-PFSSC":[228,237],"RF-PFSSC,":[230],"experimental":[233],"results":[234],"show":[235],"RF-PFSSC":[239],"outperform":[240]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
