{"id":"https://openalex.org/W3131021490","doi":"https://doi.org/10.1109/igarss39084.2020.9323251","title":"Unsupervised Hyperspectral Embedding by Learning a Deep Regression Network","display_name":"Unsupervised Hyperspectral Embedding by Learning a Deep Regression Network","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3131021490","doi":"https://doi.org/10.1109/igarss39084.2020.9323251","mag":"3131021490"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323251","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":"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/A5075013625","display_name":"Danfeng Hong","orcid":"https://orcid.org/0000-0002-3212-9584"},"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":true,"raw_author_name":"Danfeng Hong","raw_affiliation_strings":["Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Germany"],"affiliations":[{"raw_affiliation_string":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Germany","institution_ids":["https://openalex.org/I2898391981"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013885739","display_name":"Jing Yao","orcid":"https://orcid.org/0000-0003-1301-9758"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jing Yao","raw_affiliation_strings":["Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106124934","display_name":"Jocelyn Chanussot","orcid":"https://orcid.org/0000-0003-4817-2875"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jocelyn Chanussot","raw_affiliation_strings":["Univ. Grenoble, INRIA, CNRS, Grenoble INP, LJKAlpes, Grenoble, France"],"affiliations":[{"raw_affiliation_string":"Univ. Grenoble, INRIA, CNRS, Grenoble INP, LJKAlpes, Grenoble, France","institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I1294671590","https://openalex.org/I106785703"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068384981","display_name":"Xiao Xiang Zhu","orcid":"https://orcid.org/0000-0001-5530-3613"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Xiao Xiang Zhu","raw_affiliation_strings":["Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075013625"],"corresponding_institution_ids":["https://openalex.org/I2898391981"],"apc_list":null,"apc_paid":null,"fwci":0.3773,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.69906905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"103","issue":null,"first_page":"2049","last_page":"2052"},"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":0.9934999942779541,"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":0.9876000285148621,"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":0.9005148410797119},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7212618589401245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6624259352684021},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.6570683717727661},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6179112195968628},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5591440796852112},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5094543099403381},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5035414099693298},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4991748332977295},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.458763062953949},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4304131865501404},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41643020510673523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35285699367523193},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25313735008239746},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08419591188430786}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9005148410797119},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7212618589401245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6624259352684021},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.6570683717727661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6179112195968628},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5591440796852112},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5094543099403381},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5035414099693298},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4991748332977295},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.458763062953949},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4304131865501404},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41643020510673523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35285699367523193},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25313735008239746},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08419591188430786},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323251","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323251","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1583106483","https://openalex.org/W1786486465","https://openalex.org/W2035875937","https://openalex.org/W2089468765","https://openalex.org/W2097308346","https://openalex.org/W2163886442","https://openalex.org/W2588117332","https://openalex.org/W2609880332","https://openalex.org/W2616976651","https://openalex.org/W2755992512","https://openalex.org/W2897962879","https://openalex.org/W2902746003","https://openalex.org/W2904086561","https://openalex.org/W2907147407","https://openalex.org/W2939570633","https://openalex.org/W2952565170","https://openalex.org/W2953303055","https://openalex.org/W2965344373","https://openalex.org/W2965945478","https://openalex.org/W2968707955","https://openalex.org/W2977355106","https://openalex.org/W2983707810","https://openalex.org/W2994639710","https://openalex.org/W2996478649","https://openalex.org/W3009883650","https://openalex.org/W3046027728","https://openalex.org/W3101012758","https://openalex.org/W3101640299","https://openalex.org/W3103294617","https://openalex.org/W3103812551","https://openalex.org/W3105021316","https://openalex.org/W3105298104","https://openalex.org/W3122774149"],"related_works":["https://openalex.org/W2375574759","https://openalex.org/W2383239174","https://openalex.org/W3088634662","https://openalex.org/W2292979300","https://openalex.org/W117517268","https://openalex.org/W3162910294","https://openalex.org/W2539700568","https://openalex.org/W2931531042","https://openalex.org/W1489327846","https://openalex.org/W4287375746"],"abstract_inverted_index":{"This":[0,73],"work":[1],"presents":[2],"a":[3,10,82],"novel":[4],"hyperspectral":[5,61,102,123,133],"embedding":[6,30,134],"technique":[7],"by":[8,67,79],"learning":[9,71],"deep":[11,83],"regression":[12,84],"network":[13],"in":[14],"an":[15,49],"unsupervised":[16],"fashion,":[17],"which":[18],"aims":[19],"at":[20],"reducing":[21],"the":[22,36,96,100,121,126,131],"computational":[23],"complexity":[24],"and":[25,51,63,128],"storage-costing":[26],"of":[27,39,59,81,93,99,130],"traditional":[28],"manifold":[29,70,97],"methods":[31],"as":[32,34],"well":[33,77],"improving":[35],"representation":[37],"ability":[38],"spectral":[40],"signatures":[41],"effectively.":[42],"The":[43,86],"proposed":[44,132],"method":[45],"attempts":[46],"to":[47,113],"learn":[48],"explicit":[50],"unified":[52],"nonlinear":[53],"mapping":[54],"from":[55,104],"all":[56],"patch-wise":[57],"correspondences":[58],"original":[60],"data":[62,124],"dimension-reduced":[64],"products":[65],"generated":[66],"some":[68],"existing":[69],"approaches.":[72],"process":[74],"can":[75],"be":[76],"performed":[78],"means":[80],"model.":[85],"learned":[87],"model":[88],"is":[89],"not":[90],"only":[91],"capable":[92],"locally":[94],"capturing":[95],"structure":[98],"whole":[101],"image":[103],"densely":[105],"patch-based":[106],"random":[107],"sampling":[108],"but":[109],"also":[110],"better":[111],"applicable":[112],"high-efficient":[114],"out-of-sample":[115],"inference.":[116],"Experimental":[117],"results":[118],"conducted":[119],"on":[120],"real":[122],"demonstrate":[125],"effectiveness":[127],"superiority":[129],"technique.":[135]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
