{"id":"https://openalex.org/W3132333640","doi":"https://doi.org/10.1109/igarss39084.2020.9323959","title":"Hyperspectral Data Classification and Regression Using Wavelet Transform","display_name":"Hyperspectral Data Classification and Regression Using Wavelet Transform","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3132333640","doi":"https://doi.org/10.1109/igarss39084.2020.9323959","mag":"3132333640"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5110392984","display_name":"Takato Yamada","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takato Yamada","raw_affiliation_strings":["University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005014399","display_name":"Akira Iwasaki","orcid":"https://orcid.org/0000-0002-1603-8041"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Iwasaki","raw_affiliation_strings":["University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011889720","display_name":"Yoshio Inoue","orcid":"https://orcid.org/0000-0003-4682-848X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshio Inoue","raw_affiliation_strings":["University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2703","last_page":"2706"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9932000041007996,"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"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9926999807357788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9250388145446777},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6584110260009766},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.605094850063324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.597387433052063},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5482022762298584},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5449388027191162},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.529775857925415},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5117619037628174},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.45813870429992676},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4338243007659912},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4144287407398224},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2529732882976532},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17051270604133606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16543376445770264}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9250388145446777},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6584110260009766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.605094850063324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.597387433052063},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5482022762298584},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5449388027191162},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.529775857925415},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5117619037628174},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.45813870429992676},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4338243007659912},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4144287407398224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2529732882976532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17051270604133606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16543376445770264}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1996021349","https://openalex.org/W2043665634","https://openalex.org/W2104269704","https://openalex.org/W2114747706","https://openalex.org/W2131725398","https://openalex.org/W3101640299","https://openalex.org/W3113090343","https://openalex.org/W4240485910","https://openalex.org/W4320339642"],"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/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440"],"abstract_inverted_index":{"Since":[0],"hyperspectral":[1,53,75],"data":[2,76],"is":[3,10,49],"composed":[4],"of":[5,61,128],"many":[6],"spectral":[7,25],"bands,":[8],"it":[9,103],"used":[11],"for":[12,42,52,92,113,132],"classification":[13,79,93],"with":[14],"high":[15],"accuracy.":[16,32],"Various":[17],"studies":[18],"using":[19],"spatial":[20,100],"information":[21,26,71],"as":[22,24,45],"well":[23],"have":[27,39],"been":[28,40],"conducted":[29],"to":[30,68,95],"improve":[31,69],"However,":[33],"in":[34,77],"recent":[35],"years":[36],"pixel-based":[37],"methods":[38,98],"reviewed":[41],"purposes":[43],"such":[44],"anomaly":[46],"detection,":[47],"which":[48],"originally":[50],"expected":[51],"data.":[54],"In":[55],"this":[56],"work,":[57],"we":[58,119],"applied":[59],"one":[60],"the":[62,70,84,86,96,107,126,129],"time-frequency":[63],"analysis":[64],"techniques,":[65],"wavelet":[66],"transform,":[67],"extraction":[72],"capability":[73],"from":[74],"both":[78],"and":[80,124],"regression":[81,122],"tasks.":[82],"As":[83],"result,":[85],"proposed":[87,108,130],"method":[88,109,131],"showed":[89],"higher":[90],"accuracy":[91],"compared":[94,120],"conventional":[97],"without":[99],"information.":[101],"Also,":[102],"was":[104,110],"confirmed":[105,125],"that":[106],"effective":[111],"even":[112],"small":[114],"size":[115],"classes.":[116],"For":[117],"regression,":[118],"various":[121],"models":[123],"effectiveness":[127],"almost":[133],"all":[134],"models.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
