{"id":"https://openalex.org/W2985521021","doi":"https://doi.org/10.1109/igarss.2019.8900113","title":"Multi-Scale Ships Detection in High-Resolution Remote Sensing Image Via Saliency-Based Region Convolutional Neural Network","display_name":"Multi-Scale Ships Detection in High-Resolution Remote Sensing Image Via Saliency-Based Region Convolutional Neural Network","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2985521021","doi":"https://doi.org/10.1109/igarss.2019.8900113","mag":"2985521021"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8900113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900113","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":"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/A5090048842","display_name":"Zezhong Li","orcid":"https://orcid.org/0000-0001-6406-7237"},"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":"Zezhong Li","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunication"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008579555","display_name":"Yanan You","orcid":"https://orcid.org/0000-0001-6473-9187"},"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":"Yanan You","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunication"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039127636","display_name":"Fang Liu","orcid":"https://orcid.org/0000-0001-8032-6681"},"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":"Fang Liu","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunication"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"246","last_page":"249"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.9997000098228455,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9994999766349792,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8421895503997803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7692622542381287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7250446677207947},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7214168906211853},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.596665620803833},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5920047163963318},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5289915800094604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5056629180908203},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47860094904899597},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45590609312057495},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.45533058047294617},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42171159386634827},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41894856095314026},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.41378942131996155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08203837275505066},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06131300330162048}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8421895503997803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7692622542381287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7250446677207947},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7214168906211853},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.596665620803833},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5920047163963318},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5289915800094604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5056629180908203},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47860094904899597},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45590609312057495},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.45533058047294617},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42171159386634827},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41894856095314026},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.41378942131996155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08203837275505066},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06131300330162048},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8900113","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900113","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":11,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1772076007","https://openalex.org/W1996031228","https://openalex.org/W2613718673","https://openalex.org/W2632026579","https://openalex.org/W2768489488","https://openalex.org/W2786644902","https://openalex.org/W2804436788","https://openalex.org/W2962749812","https://openalex.org/W3104979525","https://openalex.org/W6620707391"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4249847449","https://openalex.org/W44395729","https://openalex.org/W3185156046","https://openalex.org/W2786391746","https://openalex.org/W4381430104","https://openalex.org/W2995102745","https://openalex.org/W4226059458","https://openalex.org/W2914559142","https://openalex.org/W1990237101"],"abstract_inverted_index":{"Ship":[0],"detection":[1,27,33,60,98],"is":[2,43,65,75,92],"of":[3,41,57,103,112],"great":[4],"significance":[5],"in":[6,34,67,100],"both":[7],"military":[8],"and":[9,84,116],"civilian":[10],"application":[11],"domains.":[12],"Deep":[13],"Convolutional":[14],"Neural":[15],"Network":[16],"(DCNN)":[17],"method":[18],"with":[19],"region":[20,47],"proposal,":[21],"e.g.":[22],"Faster":[23],"R-CNN,":[24],"achieves":[25],"ship":[26,59],"well.":[28],"However,":[29],"for":[30,88],"multi-scale":[31,58],"target":[32,97],"high-resolution":[35],"remote":[36],"sensing":[37],"image,":[38],"the":[39,46,51,55,85,110],"limitation":[40],"accuracy":[42,115],"induced":[44],"by":[45,50,120],"proposal":[48],"restricted":[49],"training":[52],"set.":[53],"Therefore,":[54],"mechanism":[56],"based":[61],"on":[62],"saliency":[63,72],"estimation":[64,73],"proposed":[66],"our":[68],"work.":[69],"Firstly,":[70],"a":[71,96],"algorithm":[74],"used":[76],"to":[77],"distinguish":[78],"which":[79],"image":[80,86],"contains":[81],"large":[82],"ships":[83],"pyramid":[87],"each":[89],"input":[90],"one":[91],"established.":[93],"Then,":[94],"using":[95],"network":[99],"different":[101],"scales":[102],"images.":[104],"The":[105],"results":[106],"are":[107,118],"merged":[108],"at":[109],"end":[111],"network.":[113],"Finally,":[114],"validity":[117],"verified":[119],"real":[121],"data":[122],"processing.":[123]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
