{"id":"https://openalex.org/W2540780780","doi":"https://doi.org/10.1109/whispers.2016.8071716","title":"Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields","display_name":"Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2540780780","doi":"https://doi.org/10.1109/whispers.2016.8071716","mag":"2540780780"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2016.8071716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2016.8071716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1610.06985","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086376779","display_name":"Utsav B. Gewali","orcid":"https://orcid.org/0000-0003-1749-4329"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Utsav B. Gewali","raw_affiliation_strings":["Chester F. Carlson Center for Imaging Science"],"affiliations":[{"raw_affiliation_string":"Chester F. Carlson Center for Imaging Science","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018170783","display_name":"Sildomar T. Monteiro","orcid":"https://orcid.org/0000-0001-7694-9536"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sildomar T. Monteiro","raw_affiliation_strings":["Dept. of Electrical Engineering, Rochester Institute of Technology, Rochester, NY"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Rochester Institute of Technology, Rochester, NY","institution_ids":["https://openalex.org/I155173764"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086376779"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6548,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76258224,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9955000281333923,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/mathematics","display_name":"Mathematics","score":0.6137430667877197},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5673699378967285},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.5638496279716492},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5605087280273438},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.5381056070327759},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5213728547096252},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5150794386863708},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.48529237508773804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4615831971168518},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4465503394603729},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4373128116130829},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4312840700149536},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.4298310875892639},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4298042058944702},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.4151625633239746},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.306995153427124},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15806248784065247},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1333843171596527},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.12806153297424316},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11478623747825623}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6137430667877197},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5673699378967285},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.5638496279716492},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5605087280273438},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.5381056070327759},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5213728547096252},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5150794386863708},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.48529237508773804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4615831971168518},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4465503394603729},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4373128116130829},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4312840700149536},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.4298310875892639},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4298042058944702},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.4151625633239746},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.306995153427124},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15806248784065247},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1333843171596527},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.12806153297424316},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11478623747825623},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/whispers.2016.8071716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2016.8071716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1610.06985","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1610.06985","pdf_url":"https://arxiv.org/pdf/1610.06985","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:scholarworks.rit.edu:other-1900","is_oa":false,"landing_page_url":"https://scholarworks.rit.edu/other/874","pdf_url":null,"source":{"id":"https://openalex.org/S4306402456","display_name":"RIT Scholar Works (Rochester Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I155173764","host_organization_name":"Rochester Institute of Technology","host_organization_lineage":["https://openalex.org/I155173764"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Presentations and other scholarship","raw_type":"text"},{"id":"pmh:oai:repository.rit.edu:other-1900","is_oa":false,"landing_page_url":"https://repository.rit.edu/other/874","pdf_url":null,"source":{"id":"https://openalex.org/S4306402456","display_name":"RIT Scholar Works (Rochester Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I155173764","host_organization_name":"Rochester Institute of Technology","host_organization_lineage":["https://openalex.org/I155173764"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Presentations and other scholarship","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1610.06985","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1610.06985","pdf_url":"https://arxiv.org/pdf/1610.06985","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1412998553","https://openalex.org/W1510526001","https://openalex.org/W1564111327","https://openalex.org/W1746819321","https://openalex.org/W2001298023","https://openalex.org/W2086504823","https://openalex.org/W2090424610","https://openalex.org/W2103568877","https://openalex.org/W2107386393","https://openalex.org/W2107884096","https://openalex.org/W2114819256","https://openalex.org/W2118585731","https://openalex.org/W2131864940","https://openalex.org/W2132648706","https://openalex.org/W2143516773","https://openalex.org/W2153635508","https://openalex.org/W2158400785","https://openalex.org/W2168809519","https://openalex.org/W2508058002","https://openalex.org/W2510592672","https://openalex.org/W3120421331","https://openalex.org/W6630424276","https://openalex.org/W6675823452","https://openalex.org/W6677656871"],"related_works":["https://openalex.org/W1971337326","https://openalex.org/W2354365489","https://openalex.org/W2039299898","https://openalex.org/W3096432517","https://openalex.org/W782485990","https://openalex.org/W4285599564","https://openalex.org/W987019958","https://openalex.org/W4390904026","https://openalex.org/W4390918438","https://openalex.org/W2551191394"],"abstract_inverted_index":{"In":[0,117],"this":[1],"paper,":[2],"we":[3],"propose":[4],"and":[5,64,84,109],"compare":[6,93],"two":[7,121],"spectral":[8,19,48,55,78,129],"angle":[9,20,49,56,79,130],"based":[10,57],"approaches":[11],"for":[12],"spatial-spectral":[13],"classification.":[14],"Our":[15],"methods":[16,96,103,142],"use":[17,45,75,105],"the":[18,33,46,60,65,76,81,85,89,94,98,140],"to":[21,44,73,139],"generate":[22],"unary":[23,90,132],"energies":[24],"in":[25],"a":[26,37,54,144],"grid-structured":[27],"Markov":[28,100],"random":[29,101],"field":[30,102],"defined":[31],"over":[32],"pixel":[34,83],"labels":[35],"of":[36],"hyperspectral":[38],"image.":[39],"The":[40,69],"first":[41],"approach":[42,71],"is":[43,72,124],"exponential":[47,114],"mapper":[50],"(ESAM)":[51],"kernel/covariance":[52,115],"function,":[53,58],"with":[59,97,112,120],"support":[61,106],"vector":[62,107],"machine":[63],"Gaussian":[66,110],"process":[67],"classifier.":[68],"second":[70],"directly":[74],"minimum":[77,128],"between":[80],"test":[82],"training":[86],"pixels":[87],"as":[88,131],"energy.":[91],"We":[92],"proposed":[95],"state-of-the-art":[99],"that":[104,126],"machines":[108],"processes":[111],"squared":[113],"function.":[116],"our":[118],"experiments":[119],"datasets,":[122],"it":[123],"seen":[125],"using":[127],"energy":[133],"produces":[134],"better":[135],"or":[136],"comparable":[137],"results":[138],"existing":[141],"at":[143],"smaller":[145],"running":[146],"time.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
