{"id":"https://openalex.org/W3205771168","doi":"https://doi.org/10.1109/igarss47720.2021.9554345","title":"Classification of Multi-Resolution Hyperspectral Data by Convolutional Neural Networks","display_name":"Classification of Multi-Resolution Hyperspectral Data by Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205771168","doi":"https://doi.org/10.1109/igarss47720.2021.9554345","mag":"3205771168"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554345","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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":["the University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","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":["the University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the 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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2015","issue":null,"first_page":"3685","last_page":"3688"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9966999888420105,"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":0.9180645942687988},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7310807108879089},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7159727811813354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7101877927780151},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6701421141624451},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.6356828212738037},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5765047073364258},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5386554598808289},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49653130769729614}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9180645942687988},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7310807108879089},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7159727811813354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7101877927780151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6701421141624451},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.6356828212738037},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5765047073364258},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5386554598808289},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49653130769729614}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554345","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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":6,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1996021349","https://openalex.org/W2911003490","https://openalex.org/W2991983536","https://openalex.org/W2998748867","https://openalex.org/W4241866650"],"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/W2077021924"],"abstract_inverted_index":{"To":[0],"realize":[1],"the":[2,56,59,71,80,100],"pixel-based":[3],"classification":[4],"of":[5,46,58,110],"hyperspectral":[6,42,51],"data,":[7,15,35],"wavelet":[8,27,72],"transform":[9,28,73],"is":[10,29],"applied":[11,40],"to":[12,20,41,79,83],"produce":[13],"two-dimensional":[14],"which":[16,44],"makes":[17],"it":[18,36],"possible":[19],"combine":[21],"convolutional":[22],"neural":[23],"networks":[24],"(CNNs).":[25],"Although":[26],"usually":[30],"used":[31,75],"for":[32],"time":[33],"series":[34],"can":[37,61],"also":[38],"be":[39,62],"data":[43,52,78,93],"consist":[45],"many":[47],"bands.":[48],"By":[49],"analyzing":[50],"with":[53,106],"multiple":[54],"resolutions,":[55],"characteristics":[57],"spectrum":[60],"captured":[63],"on":[64],"various":[65],"scales.":[66],"The":[67],"features":[68],"generated":[69],"by":[70],"are":[74],"as":[76],"input":[77],"CNN":[81],"classifier":[82],"identify":[84],"class-specific":[85],"patterns.":[86],"We":[87],"verified":[88],"this":[89],"approach":[90],"using":[91],"two":[92],"sets":[94],"and":[95],"confirmed":[96],"its":[97],"effectiveness.":[98],"Futhermore,":[99],"proposed":[101],"method":[102],"performed":[103],"well":[104],"even":[105],"a":[107],"small":[108],"number":[109],"training":[111],"samples.":[112]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}