{"id":"https://openalex.org/W3082525700","doi":"https://doi.org/10.1145/3411201.3411210","title":"Generating Hyperspectral Data Based on 3D CNN and Improved Wasserstein Generative Adversarial Network Using Homemade High-resolution Datasets","display_name":"Generating Hyperspectral Data Based on 3D CNN and Improved Wasserstein Generative Adversarial Network Using Homemade High-resolution Datasets","publication_year":2020,"publication_date":"2020-05-13","ids":{"openalex":"https://openalex.org/W3082525700","doi":"https://doi.org/10.1145/3411201.3411210","mag":"3082525700"},"language":"en","primary_location":{"id":"doi:10.1145/3411201.3411210","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3411201.3411210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Wireless Communication and Sensor Networks","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/A5102018698","display_name":"Yin Li","orcid":"https://orcid.org/0000-0003-1435-2966"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yin Li","raw_affiliation_strings":["State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101558366","display_name":"Da Huang","orcid":"https://orcid.org/0000-0003-0688-4639"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Da Huang","raw_affiliation_strings":["State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102018698"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.24080683,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"49","last_page":"55"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9965999722480774,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9965999722480774,"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":0.9957000017166138,"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":0.9955999851226807,"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":0.8285041451454163},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.8024654388427734},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7546712160110474},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7533043622970581},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5791628360748291},{"id":"https://openalex.org/keywords/superresolution","display_name":"Superresolution","score":0.47534844279289246},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44483089447021484},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4214113652706146},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.42001569271087646},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3384118378162384},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3345412611961365},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2850220203399658},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.073995441198349}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8285041451454163},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.8024654388427734},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7546712160110474},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7533043622970581},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5791628360748291},{"id":"https://openalex.org/C141239990","wikidata":"https://www.wikidata.org/wiki/Q957423","display_name":"Superresolution","level":3,"score":0.47534844279289246},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44483089447021484},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4214113652706146},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.42001569271087646},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3384118378162384},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3345412611961365},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2850220203399658},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.073995441198349}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3411201.3411210","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3411201.3411210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Wireless Communication and Sensor Networks","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1950365613","https://openalex.org/W1966580635","https://openalex.org/W1981276685","https://openalex.org/W2001298023","https://openalex.org/W2012797058","https://openalex.org/W2029316659","https://openalex.org/W2039361386","https://openalex.org/W2048976066","https://openalex.org/W2069231830","https://openalex.org/W2121338139","https://openalex.org/W2164437025","https://openalex.org/W2168901348","https://openalex.org/W2171566342","https://openalex.org/W2395957711","https://openalex.org/W2500751094","https://openalex.org/W2515174616","https://openalex.org/W2518718668","https://openalex.org/W2572303978","https://openalex.org/W2768879326","https://openalex.org/W2773315559","https://openalex.org/W2887039607","https://openalex.org/W2962793481","https://openalex.org/W2963470893","https://openalex.org/W2963622297","https://openalex.org/W4255907598","https://openalex.org/W4285784613","https://openalex.org/W6668147715"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W4385421777","https://openalex.org/W2969215546","https://openalex.org/W4312298769","https://openalex.org/W2891027158","https://openalex.org/W4390815036","https://openalex.org/W3133894820","https://openalex.org/W4205204092"],"abstract_inverted_index":{"Hyperspectral":[0],"images":[1,29,107,126,174,189],"contain":[2],"rich":[3,42],"information":[4],"on":[5],"the":[6,16,46,109,137,146,157,161,168,177,183,186,195,203,206],"fingerprints":[7],"of":[8,18,34,79,111,115,163,185,205],"materials":[9],"and":[10,20,24,50,75,133],"are":[11,72],"being":[12],"popularly":[13],"used":[14],"in":[15],"exploration":[17],"oil":[19],"gas,":[21],"environmental":[22],"monitoring,":[23],"remote":[25],"sensing.":[26],"Since":[27],"hyperspectral":[28,67,106,119,125,173,188],"cover":[30],"a":[31,112,141],"wide":[32],"range":[33],"wavelengths":[35],"with":[36,108,156],"high":[37],"resolution,":[38],"they":[39],"can":[40],"provide":[41],"features":[43,65],"for":[44],"enhancing":[45],"subsequent":[47],"target":[48],"detection":[49],"classification":[51],"procedure.":[52],"The":[53,199],"recently":[54],"proposed":[55,147,207],"deep":[56,178],"learning":[57,153,179],"algorithms":[58,71],"have":[59],"been":[60],"frequently":[61],"utilized":[62],"to":[63,81,104,194],"extract":[64],"from":[66],"images.":[68,120],"However,":[69],"these":[70],"all":[73],"data-hungry,":[74],"need":[76],"large":[77],"volume":[78],"data":[80],"learn":[82],"an":[83,94],"even":[84],"usable":[85],"model.":[86,208],"To":[87,160],"overcome":[88],"this":[89,91,166],"deficiency,":[90],"paper":[92],"proposes":[93],"improved":[95],"3D":[96,148],"Wasserstein":[97],"generative":[98],"adversarial":[99],"network":[100],"(3D":[101],"WGAN-GP)":[102],"model":[103,150],"generate":[105],"help":[110],"small":[113],"amount":[114],"training":[116],"homemade":[117],"unlabeled":[118],"In":[121],"particular,":[122],"WGAN-GP":[123,149],"generates":[124],"through":[127,140],"three-dimensional":[128],"convolutional":[129],"neural":[130],"networks":[131,155],"(3D-CNN)":[132],"discriminates":[134],"it":[135],"against":[136],"real":[138,197],"ones":[139],"discriminator":[142],"network.":[143],"We":[144,181],"train":[145],"by":[151,175,190],"jointly":[152],"both":[154],"Adam":[158],"algorithm.":[159,180],"best":[162],"our":[164],"knowledge,":[165],"is":[167],"first":[169],"work":[170],"that":[171],"augments":[172],"using":[176],"evaluate":[182],"quality":[184],"generated":[187],"comparing":[191],"their":[192],"spectrums":[193],"corresponding":[196],"ones.":[198],"experimental":[200],"results":[201],"confirm":[202],"effectiveness":[204]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}