{"id":"https://openalex.org/W4312791320","doi":"https://doi.org/10.1109/igarss46834.2022.9884266","title":"Region of Interest Extraction Based on Bayesian Joint Saliency Detection for Remote Sensing Images","display_name":"Region of Interest Extraction Based on Bayesian Joint Saliency Detection for Remote Sensing Images","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312791320","doi":"https://doi.org/10.1109/igarss46834.2022.9884266"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884266","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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/A5032290068","display_name":"Wanning Zhu","orcid":"https://orcid.org/0009-0006-8038-9766"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wanning Zhu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087779583","display_name":"Libao Zhang","orcid":"https://orcid.org/0000-0002-0888-2330"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libao Zhang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072553925","display_name":"Yinggang Zhani","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145825","display_name":"China Academy Of Machinery Science & Technology (China)","ror":"https://ror.org/04gpxrn68","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210145825"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinggang Zhani","raw_affiliation_strings":["China Academy of Machinery Science and Technology Group Co., Ltd.,Beijing,P. R. China,100044"],"affiliations":[{"raw_affiliation_string":"China Academy of Machinery Science and Technology Group Co., Ltd.,Beijing,P. R. China,100044","institution_ids":["https://openalex.org/I4210145825"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032290068"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15521175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"2183","last_page":"2186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":1.0,"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":1.0,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9861000180244446,"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7724741697311401},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6967439651489258},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6072031259536743},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.5862970948219299},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5827410221099854},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5679047703742981},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5156573057174683},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5073825716972351},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42770296335220337},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4197738766670227},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32447952032089233}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7724741697311401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6967439651489258},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6072031259536743},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.5862970948219299},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5827410221099854},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5679047703742981},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5156573057174683},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5073825716972351},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42770296335220337},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4197738766670227},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32447952032089233},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884266","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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":16,"referenced_works":["https://openalex.org/W1651093245","https://openalex.org/W1981228217","https://openalex.org/W1992992668","https://openalex.org/W2039313011","https://openalex.org/W2047670868","https://openalex.org/W2100470808","https://openalex.org/W2135957164","https://openalex.org/W2793668851","https://openalex.org/W2948500402","https://openalex.org/W2994928934","https://openalex.org/W3015575979","https://openalex.org/W3037448869","https://openalex.org/W3118335270","https://openalex.org/W4239147634","https://openalex.org/W6637231022","https://openalex.org/W6680437723"],"related_works":["https://openalex.org/W2107628111","https://openalex.org/W2394004323","https://openalex.org/W2398764543","https://openalex.org/W2027335291","https://openalex.org/W4210328553","https://openalex.org/W1980417906","https://openalex.org/W2171903035","https://openalex.org/W1996130883","https://openalex.org/W2106598802","https://openalex.org/W1576025770"],"abstract_inverted_index":{"Saliency":[0],"detection":[1,48],"is":[2,110],"an":[3],"essential":[4],"tool":[5],"to":[6,22,31,50,65,100,112],"extract":[7,51],"regions":[8,70],"of":[9,34,95],"interest":[10],"(ROIs)":[11],"in":[12,104,144],"remote":[13],"sensing":[14],"(RS)":[15],"images.":[16,40,97],"However,":[17],"many":[18],"methods":[19,139],"are":[20],"applied":[21,111],"single":[23],"image":[24],"and":[25,121,129,140],"cannot":[26],"detect":[27],"ROIs":[28],"accurately":[29],"due":[30,99],"the":[32,44,56,75,81,92,101,114,118],"ignorance":[33],"high":[35],"correlation":[36],"among":[37],"different":[38],"RS":[39,105],"Thus,":[41],"we":[42,54,79],"propose":[43],"Bayesian":[45,107],"joint":[46],"saliency":[47,58,83,116],"method":[49,136],"ROIs.":[52],"Firstly,":[53],"generate":[55],"prior":[57,115],"based":[59],"on":[60],"global":[61],"color":[62],"contrast":[63],"according":[64],"co-clustering,":[66],"which":[67,89],"ensures":[68],"that":[69,134],"with":[71,117,124],"similar":[72],"features":[73],"have":[74],"same":[76],"saliency.":[77],"Secondly,":[78],"produce":[80],"likelihood":[82,119],"by":[84],"constructing":[85],"intensity":[86,93],"co-occurrence":[87],"histogram,":[88],"can":[90],"explore":[91],"distribution":[94],"multiple":[96],"Finally,":[98],"complex":[102],"scenes":[103],"images,":[106],"enhancement":[108],"strategy":[109],"combine":[113],"saliency,":[120],"obtain":[122],"ROI":[123,145],"less":[125],"background":[126],"inference.":[127],"Quantitative":[128],"qualitative":[130],"experiments":[131],"results":[132],"indicate":[133],"our":[135],"outperforms":[137],"competing":[138],"shows":[141],"good":[142],"performance":[143],"extraction.":[146]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}