{"id":"https://openalex.org/W2296533371","doi":"https://doi.org/10.1109/igarss.2015.7326632","title":"Segmentation as postprocessing for hyperspectral image classification","display_name":"Segmentation as postprocessing for hyperspectral image classification","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2296533371","doi":"https://doi.org/10.1109/igarss.2015.7326632","mag":"2296533371"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2015.7326632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7326632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-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/A5031229555","display_name":"Luis Ignacio Jim\u00e9nez","orcid":"https://orcid.org/0000-0001-6386-9115"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"L. I. Jimenez","raw_affiliation_strings":["Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087537466","display_name":"V. A. Ayma","orcid":"https://orcid.org/0000-0003-2987-2761"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"V. A. Ayma","raw_affiliation_strings":["Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033023278","display_name":"Pedro Achanccaray","orcid":"https://orcid.org/0000-0002-7324-9611"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"P. Achanccaray","raw_affiliation_strings":["Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038352101","display_name":"Gilson Alexandre Ostwald Pedro da Costa","orcid":"https://orcid.org/0000-0001-7341-9118"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"G. A. O. P. Costa","raw_affiliation_strings":["Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067983564","display_name":"Raul Queiroz Feitosa","orcid":"https://orcid.org/0000-0001-8344-5096"},"institutions":[{"id":"https://openalex.org/I40034438","display_name":"Universidade do Estado do Rio de Janeiro","ror":"https://ror.org/0198v2949","country_code":"BR","type":"education","lineage":["https://openalex.org/I40034438"]},{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"R. Q. Feitosa","raw_affiliation_strings":["Department of Computer Engineering, Rio de Janeiro State University, Rio de Janeiro, Brasil","Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Rio de Janeiro State University, Rio de Janeiro, Brasil","institution_ids":["https://openalex.org/I40034438"]},{"raw_affiliation_string":"Department of Electrical Engineering, Catholic University of Rio de Janeiro, Rio de Janeiro, Brasil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054292278","display_name":"Antonio Plaza","orcid":"https://orcid.org/0000-0002-9613-1659"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"A. Plaza","raw_affiliation_strings":["Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5031229555"],"corresponding_institution_ids":["https://openalex.org/I80606768"],"apc_list":null,"apc_paid":null,"fwci":0.4146,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71382996,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"3723","last_page":"3726"},"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":0.9925000071525574,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9847000241279602,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9358181953430176},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7346286773681641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7243421673774719},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6744128465652466},{"id":"https://openalex.org/keywords/imaging-spectrometer","display_name":"Imaging spectrometer","score":0.6185214519500732},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.592197060585022},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5689776539802551},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5478231906890869},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5246539115905762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5065582990646362},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4976942837238312},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.47461381554603577},{"id":"https://openalex.org/keywords/spectral-signature","display_name":"Spectral signature","score":0.45745670795440674},{"id":"https://openalex.org/keywords/imaging-spectroscopy","display_name":"Imaging spectroscopy","score":0.45243126153945923},{"id":"https://openalex.org/keywords/spectrometer","display_name":"Spectrometer","score":0.2551306486129761},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22935178875923157},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1424274742603302},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.08906850218772888},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08225104212760925}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9358181953430176},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7346286773681641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7243421673774719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6744128465652466},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.6185214519500732},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.592197060585022},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5689776539802551},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5478231906890869},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5246539115905762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5065582990646362},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4976942837238312},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.47461381554603577},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.45745670795440674},{"id":"https://openalex.org/C158479148","wikidata":"https://www.wikidata.org/wiki/Q609991","display_name":"Imaging spectroscopy","level":3,"score":0.45243126153945923},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.2551306486129761},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22935178875923157},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1424274742603302},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.08906850218772888},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08225104212760925}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2015.7326632","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2015.7326632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W628438000","https://openalex.org/W1561442812","https://openalex.org/W1998030734","https://openalex.org/W2001298023","https://openalex.org/W2114819256","https://openalex.org/W2131697388","https://openalex.org/W2148791530","https://openalex.org/W2149471024","https://openalex.org/W2185979655","https://openalex.org/W2478493250","https://openalex.org/W2981849677","https://openalex.org/W6681941895","https://openalex.org/W6686709778","https://openalex.org/W6769377118"],"related_works":["https://openalex.org/W2746742660","https://openalex.org/W2082586825","https://openalex.org/W2738168532","https://openalex.org/W2561005839","https://openalex.org/W2138540356","https://openalex.org/W2799746630","https://openalex.org/W2553040330","https://openalex.org/W2343078570","https://openalex.org/W1995088959","https://openalex.org/W158571083"],"abstract_inverted_index":{"Hyperspectral":[0,83],"imaging":[1],"is":[2,69,86],"a":[3,44,53,64,74,137,142,152,166],"new":[4,138],"technique":[5,139],"in":[6,43,97,110,121],"remote":[7],"sensing":[8],"that":[9,140],"collects":[10],"hundreds":[11],"of":[12,22,35,57,72,79,127,175],"images":[13],"at":[14],"differents":[15],"wavelength":[16,45],"values":[17],"for":[18],"the":[19,23,27,58,70,98,125,128,147,158,173],"same":[20],"area":[21,91],"Earth.":[24],"For":[25],"instance":[26],"Airborne":[28],"Visible":[29],"Infra-Red":[30],"Imaging":[31],"Spectrometer":[32],"(AVIRIS)":[33],"sensor":[34],"NASA":[36],"capable":[37],"to":[38,93,123,145],"obtain":[39],"224":[40],"spectral":[41,65,114],"channels":[42],"range":[46],"between":[47],"40":[48],"and":[49,89,115],"250":[50],"nanometers.":[51],"As":[52],"result":[54],"each":[55,95],"pixel":[56,96],"image":[59,76,84,99],"can":[60],"be":[61],"represented":[62],"as":[63,157],"signature.":[66],"Image":[67],"segmentation":[68,143],"process":[71],"dividing":[73],"digital":[75],"into":[77],"groups":[78],"pixels":[80],"or":[81,119],"objects.":[82],"classification":[85,129,148],"an":[87,101],"important":[88],"active":[90],"dedicated":[92],"identifying":[94],"with":[100,165],"exclusive":[102],"material/object":[103],"class.":[104],"Several":[105],"efforts":[106],"had":[107],"been":[108],"done":[109],"this":[111,132],"field":[112],"using":[113,151],"spatial":[116],"information":[117],"separately":[118],"simultaneously":[120],"order":[122],"improve":[124],"performance":[126],"techniques.":[130],"In":[131],"work":[133],"we":[134],"have":[135],"developed":[136],"uses":[141],"algorithm":[144],"post-process":[146],"results":[149,164],"obtained":[150],"widely":[153],"used":[154],"classifier":[155],"such":[156],"support":[159],"vector":[160],"machine":[161],"(SVM).":[162],"Experimental":[163],"real":[167],"hyperspectral":[168],"data":[169],"set":[170],"collected":[171],"over":[172],"city":[174],"Pavia,":[176],"Italy,":[177],"are":[178],"provided.":[179]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
