{"id":"https://openalex.org/W3133064677","doi":"https://doi.org/10.1109/igarss39084.2020.9323160","title":"A Learnable Blur Kernel for Remote Sensing Image Retrieval","display_name":"A Learnable Blur Kernel for Remote Sensing Image Retrieval","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3133064677","doi":"https://doi.org/10.1109/igarss39084.2020.9323160","mag":"3133064677"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323160","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/A5016326528","display_name":"Zelin Peng","orcid":"https://orcid.org/0009-0002-4066-7929"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zelin Peng","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069808220","display_name":"Guanchun Wang","orcid":"https://orcid.org/0000-0002-9606-7052"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanchun Wang","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049776440","display_name":"Xiangrong Zhang","orcid":"https://orcid.org/0000-0003-0379-2042"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangrong Zhang","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059262797","display_name":"Xu Tang","orcid":"https://orcid.org/0000-0003-1375-0778"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Tang","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005573647","display_name":"Li Gao","orcid":"https://orcid.org/0000-0003-0105-3748"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li Gao","raw_affiliation_strings":["State Key Laboratory of Geo-Information Engineering, Xi'an, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Geo-Information Engineering, Xi'an, Shaanxi Province, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050630882","display_name":"Licheng Jiao","orcid":"https://orcid.org/0000-0003-3354-9617"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licheng Jiao","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi Province, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1937","last_page":"1940"},"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":0.9994000196456909,"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":0.9994000196456909,"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":0.9993000030517578,"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":0.9977999925613403,"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/computer-science","display_name":"Computer science","score":0.8033219575881958},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7094211578369141},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6479607224464417},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6410105228424072},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6126824617385864},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.5992554426193237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.543321430683136},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.49408605694770813},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41816800832748413},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41389814019203186},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.31247633695602417},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09594801068305969}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8033219575881958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7094211578369141},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6479607224464417},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6410105228424072},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6126824617385864},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.5992554426193237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.543321430683136},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.49408605694770813},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41816800832748413},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41389814019203186},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31247633695602417},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09594801068305969},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323160","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":[{"id":"https://openalex.org/G1040076515","display_name":null,"funder_award_id":"B07048","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G8883345546","display_name":null,"funder_award_id":"61772400,61772399,61871306","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1817128815","https://openalex.org/W1980038761","https://openalex.org/W2033242207","https://openalex.org/W2072055485","https://openalex.org/W2091280333","https://openalex.org/W2763822693","https://openalex.org/W2766671789","https://openalex.org/W2883105896","https://openalex.org/W2913314773","https://openalex.org/W2962858109","https://openalex.org/W2963256013"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W4312417841"],"abstract_inverted_index":{"With":[0],"the":[1,31,49,70,78,86,117,134],"explosive":[2],"increase":[3],"of":[4,33,59,65,72],"remote":[5,9,119],"sensing":[6,10,120],"images,":[7],"content-based":[8],"image":[11],"retrieval":[12,126],"(CBRSIR)":[13],"has":[14],"aroused":[15],"widespread":[16],"attention.":[17],"Convolutional":[18],"Neural":[19],"Network":[20],"(CNN)":[21],"based":[22],"methods":[23],"are":[24],"widely":[25,50],"used":[26,38,51],"in":[27,43,74],"CBRSIR":[28],"due":[29,47],"to":[30,48],"development":[32],"deep":[34],"learning.":[35],"However,":[36],"common":[37],"CNN":[39],"models":[40],"have":[41],"difficulties":[42],"holding":[44],"shift-invariant":[45,73],"property":[46],"down-sampling":[52],"method,":[53],"which":[54,103],"means":[55],"a":[56,63],"little":[57],"shift":[58],"input":[60],"may":[61],"cause":[62],"mutation":[64],"feature":[66,87],"representation.":[67],"To":[68],"mitigate":[69],"absence":[71],"down-sampling,":[75],"we":[76],"propose":[77],"learnable":[79],"blur":[80],"kernel":[81],"(LBK),":[82],"that":[83,133],"can":[84,104],"enhance":[85],"extraction":[88],"capability":[89],"by":[90],"leveraging":[91],"more":[92],"context":[93],"information.":[94],"We":[95],"build":[96],"on":[97,116],"this":[98],"concept":[99],"without":[100],"extra":[101],"cost,":[102],"be":[105],"simply":[106],"integrated":[107],"with":[108,124],"modern":[109],"CNNs":[110],"architecture.":[111],"Our":[112],"method":[113,136],"is":[114],"validated":[115],"public":[118],"dataset":[121],"and":[122],"compared":[123],"other":[125],"methods.":[127],"The":[128],"overall":[129],"experimental":[130],"results":[131],"show":[132],"proposed":[135],"achieves":[137],"outstanding":[138],"performance.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}