{"id":"https://openalex.org/W2048633921","doi":"https://doi.org/10.1109/lgrs.2014.2371242","title":"Analyzing Hyperspectral and Hypertemporal Data by Decoupling Feature Redundancy and Feature Relevance","display_name":"Analyzing Hyperspectral and Hypertemporal Data by Decoupling Feature Redundancy and Feature Relevance","publication_year":2015,"publication_date":"2015-01-06","ids":{"openalex":"https://openalex.org/W2048633921","doi":"https://doi.org/10.1109/lgrs.2014.2371242","mag":"2048633921"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2014.2371242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2371242","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5040213111","display_name":"Matthias Held","orcid":"https://orcid.org/0000-0002-8541-8830"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Matthias Held","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108536470","display_name":"Andreas Rabe","orcid":null},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Rabe","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067730563","display_name":"Cornelius Senf","orcid":"https://orcid.org/0000-0002-2389-2158"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Cornelius Senf","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015469539","display_name":"Sebastian van der Linden","orcid":"https://orcid.org/0000-0001-6576-8377"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian van der Linden","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041171844","display_name":"Patrick Hostert","orcid":"https://orcid.org/0000-0002-5730-5484"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Patrick Hostert","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geogr. Dept., Humboldt-Univ. zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040213111"],"corresponding_institution_ids":["https://openalex.org/I39343248"],"apc_list":null,"apc_paid":null,"fwci":1.2439,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.83373212,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"12","issue":"5","first_page":"983","last_page":"987"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9682000279426575,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7814931869506836},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7248478531837463},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.7098917365074158},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700299084186554},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6642681360244751},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.624492347240448},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5998292565345764},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5217986106872559},{"id":"https://openalex.org/keywords/minimum-redundancy-feature-selection","display_name":"Minimum redundancy feature selection","score":0.5217260122299194},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.49372705817222595},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4928581714630127},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3376118540763855}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7814931869506836},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7248478531837463},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7098917365074158},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700299084186554},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6642681360244751},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.624492347240448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5998292565345764},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5217986106872559},{"id":"https://openalex.org/C16811321","wikidata":"https://www.wikidata.org/wiki/Q17138905","display_name":"Minimum redundancy feature selection","level":3,"score":0.5217260122299194},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.49372705817222595},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4928581714630127},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3376118540763855},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2014.2371242","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2371242","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W144440563","https://openalex.org/W1680392829","https://openalex.org/W1967662825","https://openalex.org/W2017337590","https://openalex.org/W2021597230","https://openalex.org/W2044439250","https://openalex.org/W2060542593","https://openalex.org/W2076306039","https://openalex.org/W2082880010","https://openalex.org/W2095303436","https://openalex.org/W2098133322","https://openalex.org/W2138751033","https://openalex.org/W2141567889","https://openalex.org/W2150566919","https://openalex.org/W2151978020","https://openalex.org/W2153635508","https://openalex.org/W6605866200","https://openalex.org/W6637386731"],"related_works":["https://openalex.org/W3120617324","https://openalex.org/W2156571267","https://openalex.org/W2998727463","https://openalex.org/W2510961579","https://openalex.org/W2350815964","https://openalex.org/W1838735596","https://openalex.org/W2805829984","https://openalex.org/W4388573469","https://openalex.org/W3036204000","https://openalex.org/W2747166117"],"abstract_inverted_index":{"The":[0],"high":[1],"information":[2],"redundancy":[3,65],"in":[4],"hyperspectral":[5,117],"and":[6,40,44,92,118,132],"hypertemporal":[7,120],"Earth":[8],"observation":[9],"data":[10,111],"can":[11],"limit":[12],"the":[13,25,28,42,62,67,87,94,97],"performance":[14],"of":[15,31,46,64,69,79,89,96],"supervised":[16],"learning":[17],"algorithms.":[18],"Traditional":[19],"sequential":[20,143],"feature":[21,105,144],"selection":[22],"approaches":[23],"start":[24],"search":[26,43],"on":[27],"full":[29],"set":[30],"correlated":[32],"features,":[33],"which":[34],"is":[35,72],"a":[36,116,119],"computationally":[37],"expensive":[38],"task":[39],"impedes":[41],"discovery":[45],"spectral":[47,131],"or":[48,54,81],"temporal":[49,133],"segments":[50],"relevant":[51],"for":[52,113],"classification":[53,121],"regression":[55],"tasks.":[56],"We":[57],"therefore":[58],"propose":[59],"to":[60,129,138],"decouple":[61],"reduction":[63],"from":[66],"ranking":[68],"features.":[70],"This":[71],"achieved":[73],"by:":[74],"1)":[75],"an":[76,101],"unsupervised":[77],"clustering":[78],"spectrally":[80],"temporally":[82],"correlating":[83],"neighboring":[84],"features;":[85],"2)":[86],"definition":[88],"cluster":[90],"representatives;":[91],"3)":[93],"determination":[95],"representatives'":[98],"relevance":[99],"by":[100,109],"support":[102],"vector":[103],"machine-based":[104],"forward":[106],"selection.":[107,145],"Exemplified":[108],"two":[110],"sets":[112],"solving":[114],"both,":[115],"problem,":[122],"we":[123],"show":[124],"that":[125],"our":[126],"approach":[127],"leads":[128],"well-interpretable":[130],"clusters,":[134],"with":[135],"comparable":[136],"accuracies":[137],"more":[139],"processing":[140],"extensive":[141],"traditional":[142]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}