{"id":"https://openalex.org/W2987208198","doi":"https://doi.org/10.1109/igarss.2019.8897976","title":"Robust Real-Time Object Detection Based on Deep Learning for Very High Resolution Remote Sensing Images","display_name":"Robust Real-Time Object Detection Based on Deep Learning for Very High Resolution Remote Sensing Images","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2987208198","doi":"https://doi.org/10.1109/igarss.2019.8897976","mag":"2987208198"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8897976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8897976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 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/A5056594372","display_name":"Yiming Zhao","orcid":"https://orcid.org/0000-0003-0325-4295"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiming Zhao","raw_affiliation_strings":["International School, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International School, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057816329","display_name":"Jinzheng Zhao","orcid":"https://orcid.org/0009-0004-3910-850X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinzheng Zhao","raw_affiliation_strings":["International School, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International School, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101433858","display_name":"Chunyu Zhao","orcid":"https://orcid.org/0000-0002-3160-5585"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyu Zhao","raw_affiliation_strings":["International School, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International School, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037621894","display_name":"Weiyu Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyu Xiong","raw_affiliation_strings":["International School, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International School, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090964683","display_name":"Qingli Li","orcid":"https://orcid.org/0000-0001-5063-8801"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingli Li","raw_affiliation_strings":["International School, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International School, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101644168","display_name":"Junli Yang","orcid":"https://orcid.org/0000-0001-8370-7105"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junli Yang","raw_affiliation_strings":["International School, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International School, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5056594372"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.8098,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.77685132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1314","last_page":"1317"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9991000294685364,"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":0.9972000122070312,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9962000250816345,"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":0.8120801448822021},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7759464979171753},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7753931283950806},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7441883683204651},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7431048154830933},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.7202756404876709},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5935025215148926},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.533573567867279},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4967699646949768},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4846063554286957},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.48324307799339294},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4520993232727051},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.449236661195755},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4488966763019562},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4144294261932373},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3653014302253723},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10307523608207703},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06833711266517639},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0638420581817627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8120801448822021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7759464979171753},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7753931283950806},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7441883683204651},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7431048154830933},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.7202756404876709},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5935025215148926},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.533573567867279},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4967699646949768},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4846063554286957},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.48324307799339294},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4520993232727051},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.449236661195755},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4488966763019562},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4144294261932373},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3653014302253723},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10307523608207703},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06833711266517639},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0638420581817627},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8897976","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8897976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 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":25,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1709548961","https://openalex.org/W2194775991","https://openalex.org/W2225036299","https://openalex.org/W2512351403","https://openalex.org/W2579985080","https://openalex.org/W2613718673","https://openalex.org/W2884561390","https://openalex.org/W2949194345","https://openalex.org/W2963037989","https://openalex.org/W2963446712","https://openalex.org/W2963604034","https://openalex.org/W2964350391","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6631782140","https://openalex.org/W6637373629","https://openalex.org/W6637616945","https://openalex.org/W6689094738","https://openalex.org/W6694260854","https://openalex.org/W6732243160","https://openalex.org/W6746123559","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W4321444604","https://openalex.org/W2811106690","https://openalex.org/W2936819511","https://openalex.org/W4239306820","https://openalex.org/W2969228573","https://openalex.org/W3131692135","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Recently,":[0],"the":[1,7,45,54,73,80,97,106,128,140],"development":[2],"of":[3,91,130],"deep":[4,16,46],"learning":[5,17],"boosts":[6],"object":[8,89],"detection":[9,90],"for":[10,88],"remote":[11,92],"sensing":[12,93],"images.":[13],"The":[14,25,56,119],"existing":[15],"methods":[18,27,58],"can":[19],"be":[20],"divided":[21],"into":[22,112],"two":[23],"types.":[24],"region-based":[26],"represented":[28],"by":[29],"Faster":[30],"R-CNN":[31],"have":[32],"progressive":[33],"performance":[34],"in":[35,70,83],"accuracy.":[36],"However,":[37],"their":[38],"computational":[39],"cost":[40],"is":[41,75,142],"massive":[42],"due":[43],"to":[44,114],"Convolutional":[47],"Neural":[48],"Network":[49],"(CNN)":[50],"backbones,":[51],"which":[52,134],"limits":[53],"efficiency.":[55],"regression-based":[57],"such":[59],"as":[60],"YOLO":[61],"and":[62,86],"Single":[63],"Shot":[64],"MultiBox":[65],"Detector":[66],"(SSD)":[67],"are":[68],"advantageous":[69],"speed":[71,85,141],"while":[72],"accuracy":[74,87],"not":[76],"satisfactory.":[77],"To":[78],"meet":[79],"increasing":[81],"demand":[82],"both":[84],"images,":[94],"we":[95],"employ":[96],"Reception":[98],"Field":[99,108],"Block":[100,109],"Net":[101],"(RFBNet)":[102],"detector.":[103],"It":[104],"embeds":[105],"Receptive":[107],"(RFB)":[110],"module":[111],"SSD":[113],"obtain":[115],"better":[116],"feature":[117],"representation.":[118],"experimental":[120],"results":[121],"on":[122],"NWPU":[123],"VHR-10":[124],"dataset":[125],"demonstrate":[126],"that":[127],"mAP":[129],"RFBNet-512":[131],"reaches":[132],"91.56%,":[133],"outperforms":[135],"other":[136],"state-of-the-art":[137],"networks.":[138],"Meanwhile,":[139],"also":[143],"competitive.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
