{"id":"https://openalex.org/W2547489398","doi":"https://doi.org/10.1109/igarss.2016.7730823","title":"On the detection of linear mixtures in hyperspectral images","display_name":"On the detection of linear mixtures in hyperspectral images","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2547489398","doi":"https://doi.org/10.1109/igarss.2016.7730823","mag":"2547489398"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7730823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5083495404","display_name":"Andrea Marinoni","orcid":"https://orcid.org/0000-0001-6789-0915"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Andrea Marinoni","raw_affiliation_strings":["Dipartimento di Ingegneria Industriale e dell'Informazione, Universit\u00e0 degli Studi di Pavia, Pavia, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Ingegneria Industriale e dell'Informazione, Universit\u00e0 degli Studi di Pavia, Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]}]},{"author_position":"middle","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":"Antonio Plaza","raw_affiliation_strings":["Hyperspectral Computing Lab., University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Lab., University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006623289","display_name":"Paolo Gamba","orcid":"https://orcid.org/0000-0002-9576-6337"},"institutions":[{"id":"https://openalex.org/I25217355","display_name":"University of Pavia","ror":"https://ror.org/00s6t1f81","country_code":"IT","type":"education","lineage":["https://openalex.org/I25217355"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Gamba","raw_affiliation_strings":["Dipartimento di Ingegneria Industriale e dell'Informazione, Universit\u00e0 degli Studi di Pavia, Pavia, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Ingegneria Industriale e dell'Informazione, Universit\u00e0 degli Studi di Pavia, Pavia, Italy","institution_ids":["https://openalex.org/I25217355"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083495404"],"corresponding_institution_ids":["https://openalex.org/I25217355"],"apc_list":null,"apc_paid":null,"fwci":0.323,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66710685,"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":"6990","last_page":"6993"},"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":0.9854999780654907,"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":0.9817000031471252,"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":0.9709109663963318},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.7438026070594788},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7358335256576538},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6479837894439697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5581851601600647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.549453616142273},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.479126900434494},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42416924238204956},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34407317638397217},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3367675840854645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29820650815963745},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17531904578208923}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9709109663963318},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.7438026070594788},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7358335256576538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6479837894439697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5581851601600647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.549453616142273},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.479126900434494},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42416924238204956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34407317638397217},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3367675840854645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29820650815963745},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17531904578208923},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2016.7730823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7730823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1856317893","https://openalex.org/W2028967512","https://openalex.org/W2073650477","https://openalex.org/W2089606669","https://openalex.org/W2119449590","https://openalex.org/W2127062304","https://openalex.org/W2133059825","https://openalex.org/W3100817930","https://openalex.org/W4247458820"],"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/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"In":[0,31],"order":[1],"to":[2,72,107],"provide":[3],"reliable":[4,110],"information":[5,52],"on":[6,59,96,119],"the":[7,18,60,76,101,115,120],"instantaneous":[8],"field-of-view":[9],"considered":[10],"in":[11],"hyperspectral":[12,92],"images":[13],"through":[14],"spectral":[15,61],"unmixing,":[16],"understanding":[17],"kind":[19,116],"of":[20,40,63,75,78,80,83,88,91,114,117],"mixture":[21],"that":[22],"occurs":[23],"over":[24],"each":[25,64],"pixel":[26],"plays":[27],"a":[28,34,56,109],"crucial":[29],"role.":[30],"this":[32],"paper,":[33],"new":[35],"method":[36,47],"for":[37],"fast":[38],"detection":[39],"linear":[41,81],"mixtures":[42],"is":[43,104],"introduced.":[44],"The":[45],"proposed":[46],"does":[48],"not":[49],"need":[50],"statistical":[51],"and":[53,111],"performs":[54],"an":[55],"priori":[57],"test":[58],"linearity":[62],"pixel.":[65],"It":[66],"uses":[67],"standard":[68],"least":[69],"squares":[70],"optimization":[71],"achieve":[73],"estimates":[74],"likelihood":[77],"occurrence":[79],"combinations":[82],"endmembers":[84],"by":[85],"taking":[86],"advantage":[87],"geometrical":[89],"properties":[90],"signatures.":[93],"Experimental":[94],"results":[95],"synthetic":[97],"datasets":[98],"show":[99],"how":[100],"aforesaid":[102],"algorithm":[103],"actually":[105],"able":[106],"deliver":[108],"thorough":[112],"assessment":[113],"mix":[118],"scene.":[121]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
