{"id":"https://openalex.org/W2774265021","doi":"https://doi.org/10.1109/igarss.2017.8127328","title":"Supervised classification of hyperspectral images via heterogeneous deep neural networks","display_name":"Supervised classification of hyperspectral images via heterogeneous deep neural networks","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2774265021","doi":"https://doi.org/10.1109/igarss.2017.8127328","mag":"2774265021"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5100701695","display_name":"Zhixin Li","orcid":"https://orcid.org/0000-0002-5313-6134"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhixin Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056607422","display_name":"Yu Shen","orcid":"https://orcid.org/0000-0002-2942-6422"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Shen","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014878552","display_name":"Nan Huang","orcid":"https://orcid.org/0000-0003-0871-158X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Huang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020302879","display_name":"Liang Xiao","orcid":"https://orcid.org/0000-0003-0178-9384"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Xiao","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100701695"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.4708,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72244364,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"25","issue":null,"first_page":"1812","last_page":"1815"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9965000152587891,"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":0.9901999831199646,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8182547092437744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7920242547988892},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7434294819831848},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7211448550224304},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7132648825645447},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6547233462333679},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6182757019996643},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6127406358718872},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5716192722320557},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5294886231422424},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4622257947921753},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.43440601229667664},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4273633360862732},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3259681761264801},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23288586735725403}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8182547092437744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7920242547988892},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7434294819831848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7211448550224304},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7132648825645447},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6547233462333679},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6182757019996643},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6127406358718872},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5716192722320557},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5294886231422424},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4622257947921753},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.43440601229667664},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4273633360862732},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3259681761264801},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23288586735725403}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8127328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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":11,"referenced_works":["https://openalex.org/W1966580635","https://openalex.org/W2029316659","https://openalex.org/W2068730032","https://openalex.org/W2136251662","https://openalex.org/W2152057649","https://openalex.org/W2163605009","https://openalex.org/W2464755555","https://openalex.org/W2546886445","https://openalex.org/W2963366243","https://openalex.org/W6667689585","https://openalex.org/W6684191040"],"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/W2292979300","https://openalex.org/W2005234362","https://openalex.org/W1997235926"],"abstract_inverted_index":{"In":[0],"this":[1,88],"paper,":[2],"a":[3,24,31,37,46],"new":[4],"heterogeneous":[5,89],"neural":[6,33,39],"networks":[7],"based":[8,70],"deep":[9,32,90],"learning":[10,91],"method,":[11],"named":[12],"HNNDL,":[13],"is":[14,80,93],"presented":[15],"for":[16],"supervised":[17],"classification":[18,138,143],"of":[19,27,109,112,120,142],"hyperspectral":[20],"image":[21],"(HSI)":[22],"with":[23],"small":[25],"number":[26],"labeled":[28],"samples.":[29],"Specifically,":[30],"Network":[34],"(DNN)":[35],"and":[36,58,65,100,115],"convolutional":[38],"network":[40],"(CNN)":[41],"are":[42],"combined":[43],"to":[44,96],"build":[45],"HNNDL":[47],"architecture.":[48],"The":[49,85],"proposed":[50,133],"architecture":[51,92],"contains":[52],"three":[53],"modules:":[54],"1)":[55],"dimension":[56],"reduction":[57],"feature":[59],"extraction,":[60],"2)":[61],"training":[62],"pixel-wise":[63],"DNN":[64],"CNN,":[66],"3)":[67],"bilateral":[68],"filtering":[69],"decision":[71],"level":[72],"fusion":[73],"on":[74,125],"two":[75],"soft":[76],"probability":[77],"maps":[78],"which":[79],"produced":[81],"by":[82,105],"above":[83],"classifiers.":[84],"rationale":[86],"behind":[87],"their":[94],"ability":[95,111],"learn":[97],"more":[98],"abstract":[99],"robust":[101],"local":[102],"spectral-spatial":[103],"information":[104],"taking":[106],"full":[107],"advantages":[108],"complementary":[110],"each":[113],"networks,":[114],"thus":[116],"boost":[117],"the":[118,126,132],"performance":[119],"HSI":[121,129],"classifier.":[122],"Experimental":[123],"results":[124],"widely":[127],"used":[128],"indicate":[130],"that":[131],"approach":[134],"outperforms":[135],"several":[136],"well-known":[137],"methods":[139],"in":[140],"terms":[141],"accuracy.":[144]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
