{"id":"https://openalex.org/W3094062780","doi":"https://doi.org/10.1109/access.2020.3033469","title":"Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection","display_name":"Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3094062780","doi":"https://doi.org/10.1109/access.2020.3033469","mag":"3094062780"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3033469","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3033469","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09237930.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09237930.pdf","any_repository_has_fulltext":true},"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":["Beijing Key Laboratory of Network System Architecture and Convergence, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","Beijing Laboratory of Advanced Information Networks, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6406-7237","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Network System Architecture and Convergence, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","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":["Beijing Key Laboratory of Network System Architecture and Convergence, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","Beijing Laboratory of Advanced Information Networks, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6473-9187","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Network System Architecture and Convergence, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100453131","display_name":"Fang Liu","orcid":"https://orcid.org/0000-0003-4428-8758"},"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":["Beijing Key Laboratory of Network System Architecture and Convergence, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","Beijing Laboratory of Advanced Information Networks, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4428-8758","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Network System Architecture and Convergence, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Networks, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","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":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.4792,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66002901,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"8","issue":null,"first_page":"194485","last_page":"194496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998000264167786,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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":0.9944999814033508,"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/computer-science","display_name":"Computer science","score":0.8066087365150452},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.777841329574585},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6817381381988525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6299704313278198},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5966741442680359},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5055158138275146},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.4852335453033447},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46450990438461304},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4539565443992615},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4118778705596924},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06026279926300049}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8066087365150452},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.777841329574585},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6817381381988525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6299704313278198},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5966741442680359},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5055158138275146},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.4852335453033447},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46450990438461304},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4539565443992615},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4118778705596924},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06026279926300049},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3033469","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3033469","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09237930.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:98cb74f012dd449da9c767fb3f0709fd","is_oa":true,"landing_page_url":"https://doaj.org/article/98cb74f012dd449da9c767fb3f0709fd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 194485-194496 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3033469","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3033469","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09237930.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321470","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3094062780.pdf","grobid_xml":"https://content.openalex.org/works/W3094062780.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1490100156","https://openalex.org/W1536680647","https://openalex.org/W1996031228","https://openalex.org/W2008066834","https://openalex.org/W2034436892","https://openalex.org/W2041719651","https://openalex.org/W2064365034","https://openalex.org/W2102605133","https://openalex.org/W2105454024","https://openalex.org/W2109255472","https://openalex.org/W2116724443","https://openalex.org/W2122076510","https://openalex.org/W2128340050","https://openalex.org/W2133589685","https://openalex.org/W2146103513","https://openalex.org/W2160613239","https://openalex.org/W2166650627","https://openalex.org/W2461475918","https://openalex.org/W2465132919","https://openalex.org/W2512351403","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2579985080","https://openalex.org/W2613718673","https://openalex.org/W2621123170","https://openalex.org/W2725486421","https://openalex.org/W2768489488","https://openalex.org/W2779335303","https://openalex.org/W2783231089","https://openalex.org/W2786644902","https://openalex.org/W2796347433","https://openalex.org/W2808360995","https://openalex.org/W2888527098","https://openalex.org/W2939217524","https://openalex.org/W2962749812","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2969596978","https://openalex.org/W2973030380","https://openalex.org/W2985521021","https://openalex.org/W2992240579","https://openalex.org/W3098389804","https://openalex.org/W3106250896","https://openalex.org/W4249226784","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6629341600","https://openalex.org/W6732243160","https://openalex.org/W6739844568","https://openalex.org/W6750227808","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W2969228573","https://openalex.org/W3127668761"],"abstract_inverted_index":{"Ship":[0],"detection":[1,29,59,84,110,128,183,195],"is":[2,37,60],"of":[3,55,85,101,120,129,138,168,197,202],"considerable":[4],"significance":[5],"in":[6,27,66,80,125,162],"both":[7],"military":[8],"and":[9,99,104,134,149],"civilian":[10],"application":[11],"domains.":[12],"Deep":[13],"Convolutional":[14],"Neural":[15],"Network":[16],"(DCNN)":[17],"with":[18,30,205],"region":[19,41],"proposal":[20,42],"mechanism,":[21],"e.g.,":[22],"Faster":[23],"R-CNN,":[24],"performs":[25],"outstandingly":[26],"ship":[28,57,83,109,127],"high-resolution":[31,86,130],"images.":[32,81],"However,":[33],"the":[34,40,45,53,97,102,107,118,121,136,166,171,180,194],"accuracy":[35,196],"limitation":[36],"induced":[38],"by":[39,44,156],"restricted":[43],"training":[46,203],"set":[47],"for":[48],"multi-scale":[49,56,126,185],"target":[50,182],"detection.":[51],"Therefore,":[52],"method":[54,111],"object":[58],"proposed":[61],"based":[62],"on":[63],"saliency":[64,78,122,146,173],"estimation":[65,123,147],"our":[67],"work.":[68],"Saliency":[69],"Estimation":[70],"Algorithms":[71],"(SEAs)":[72],"are":[73,176],"often":[74],"used":[75,177],"to":[76,112,153,164,178],"extract":[77,93],"features":[79],"In":[82,188],"remote":[87,131],"sensing":[88,132],"images,":[89,133],"these":[90],"algorithms":[91,148],"can":[92,192],"information":[94],"such":[95],"as":[96],"scale":[98,207],"position":[100],"targets,":[103],"then":[105],"help":[106],"DCNN-based":[108,151,181],"obtain":[113],"better":[114],"performance.":[115],"To":[116],"verify":[117],"effectiveness":[119],"algorithm":[124],"analyze":[135],"advantages":[137],"different":[139,169],"SEAs.":[140],"This":[141],"paper":[142],"introduces":[143],"13":[144],"classic":[145],"2":[150],"ones":[152],"compare":[154],"them":[155],"using":[157],"evaluation":[158],"indicators.":[159],"At":[160],"last,":[161],"order":[163],"demonstrate":[165],"performance":[167],"SEAs,":[170],"extracted":[172],"feature":[174],"maps":[175],"assist":[179],"under":[184,200],"ships":[186,199,208],"condition.":[187],"general,":[189],"this":[190],"framework":[191],"improve":[193],"large-scale":[198],"scenarios":[201],"only":[204],"small":[206],"or":[209],"without":[210],"enough":[211],"datasets.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}