{"id":"https://openalex.org/W3130418180","doi":"https://doi.org/10.1109/igarss39084.2020.9323350","title":"DNN-Based Semantic Extraction: Fast Learning from Multispectral Signatures","display_name":"DNN-Based Semantic Extraction: Fast Learning from Multispectral Signatures","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3130418180","doi":"https://doi.org/10.1109/igarss39084.2020.9323350","mag":"3130418180"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323350","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/A5075098303","display_name":"Iulia Calota","orcid":"https://orcid.org/0000-0002-3905-4554"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Iulia Calota","raw_affiliation_strings":["University POLITEHNICA of Bucharest, UPB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University POLITEHNICA of Bucharest, UPB","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064251688","display_name":"Daniela Faur","orcid":"https://orcid.org/0000-0001-5208-5753"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Daniela Faur","raw_affiliation_strings":["University POLITEHNICA of Bucharest, UPB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University POLITEHNICA of Bucharest, UPB","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032370631","display_name":"Mihai Datcu","orcid":"https://orcid.org/0000-0002-3477-9687"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mihai Datcu","raw_affiliation_strings":["German Aerospace Center, DLR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"German Aerospace Center, DLR","institution_ids":["https://openalex.org/I2898391981"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"3672","last_page":"3675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9997000098228455,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.8000097274780273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7802556157112122},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7216935753822327},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7130411267280579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6999262571334839},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6415109634399414},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5744901299476624},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5457025766372681},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48337286710739136},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4572252333164215},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3306645154953003},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11546215415000916}],"concepts":[{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.8000097274780273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7802556157112122},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7216935753822327},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7130411267280579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6999262571334839},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6415109634399414},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5744901299476624},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5457025766372681},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48337286710739136},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4572252333164215},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3306645154953003},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11546215415000916},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323350","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"},{"id":"pmh:oai:elib.dlr.de:138253","is_oa":false,"landing_page_url":"https://elib.dlr.de/138253/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Konferenzbeitrag"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2151039611","https://openalex.org/W2163605009","https://openalex.org/W2574739416","https://openalex.org/W2604645045","https://openalex.org/W2744582969","https://openalex.org/W2764034829","https://openalex.org/W2782522152","https://openalex.org/W2790730828","https://openalex.org/W2799544692","https://openalex.org/W2904445802","https://openalex.org/W2919115771","https://openalex.org/W2986943971","https://openalex.org/W3010290582","https://openalex.org/W3011225259","https://openalex.org/W6684191040","https://openalex.org/W6735637195"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W2771047279","https://openalex.org/W4388409104","https://openalex.org/W2124951708","https://openalex.org/W1544811710","https://openalex.org/W172072032","https://openalex.org/W2006066416","https://openalex.org/W3157073418","https://openalex.org/W2021642829","https://openalex.org/W2058127401"],"abstract_inverted_index":{"In":[0,34,93],"this":[1],"paper,":[2],"we":[3,39,56,68,97],"present":[4],"three":[5],"methods":[6],"that":[7,165],"reduce":[8,40],"the":[9,21,25,35,41,44,75,79,82,89,94,99,109,125,132,153,166,178],"computational":[10],"time":[11,168],"of":[12,24,43,50,84,108,122,127,155,174],"training":[13,100,113,167],"Deep":[14],"Neural":[15,60,116],"Networks":[16],"with":[17,47,62,180],"multispectral":[18],"images,":[19],"optimize":[20],"resource":[22],"occupation":[23],"dataset,":[26,111],"and":[27,67,106,131,152,177],"obtain":[28],"high":[29],"performance":[30,134],"for":[31,149],"reduced":[32,88,170],"datasets.":[33],"first":[36],"two":[37],"methods,":[38],"dimension":[42],"input":[45],"data":[46],"either":[48,63],"histograms":[49,64,85,128,181],"pixel":[51],"intensity":[52],"or":[53,65,86,129,182],"Bag-of-Words.":[54],"Then":[55],"train":[57],"a":[58,114,119,172],"Convolutional":[59,115],"Network":[61,117],"Bag-of-Words":[66,87,183],"achieve":[69],"an":[70,140],"accelerated":[71],"training.":[72],"Moreover,":[73],"storing":[74],"image":[76],"patches":[77],"from":[78],"dataset":[80,101],"in":[81,157],"form":[83,126],"memory":[90],"storage":[91],"significantly.":[92],"last":[95],"method,":[96],"subsample":[98],"randomly":[102],"to":[103],"50%,":[104],"20%":[105],"10%":[107],"original":[110],"thus":[112],"on":[118],"smaller":[120],"number":[121,154],"samples":[123],"(in":[124],"Bag-of-Words),":[130],"classification":[133],"is":[135,139,160,169],"almost":[136],"unaffected.":[137],"This":[138],"important":[141],"achievement,":[142],"as":[143],"there":[144],"are":[145],"few":[146],"labelled":[147],"datasets":[148,159,179],"Earth":[150],"Observation":[151],"images":[156],"these":[158],"small.":[161],"Our":[162],"results":[163],"show":[164],"by":[171],"maximum":[173],"387":[175],"times":[176,186],"occupy":[184],"633":[185],"less":[187],"space.":[188]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}