{"id":"https://openalex.org/W3206487411","doi":"https://doi.org/10.1109/igarss47720.2021.9553409","title":"Common Regions of Interest Extraction Based on Saliency Statistic Analysis for Multiple Remote Sensing Images","display_name":"Common Regions of Interest Extraction Based on Saliency Statistic Analysis for Multiple Remote Sensing Images","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3206487411","doi":"https://doi.org/10.1109/igarss47720.2021.9553409","mag":"3206487411"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9553409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5078911031","display_name":"Xinran Lyu","orcid":"https://orcid.org/0000-0002-8267-5698"},"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":"Xinran Lyu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100322332","display_name":"Lan Zhang","orcid":"https://orcid.org/0009-0007-4678-0419"},"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":"Lan Zhang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","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":false,"raw_author_name":"Wanning Zhu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","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"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5078911031"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49857843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"4779","last_page":"4782"},"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.995199978351593,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9848999977111816,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7371844053268433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7231126427650452},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.657029926776886},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5409115552902222},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5381584763526917},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5356435775756836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5264169573783875},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.4951910078525543},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49196597933769226},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4683018922805786},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4643106460571289},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45097607374191284},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4387471675872803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7371844053268433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7231126427650452},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.657029926776886},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5409115552902222},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5381584763526917},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5356435775756836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5264169573783875},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.4951910078525543},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49196597933769226},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4683018922805786},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4643106460571289},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45097607374191284},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4387471675872803},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9553409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4503850585","display_name":null,"funder_award_id":"L182029","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G8270592409","display_name":null,"funder_award_id":"41771407,61571050","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/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1963816830","https://openalex.org/W1974083340","https://openalex.org/W2100470808","https://openalex.org/W2118246710","https://openalex.org/W2128272608","https://openalex.org/W2135957164","https://openalex.org/W2146103513","https://openalex.org/W2509486428","https://openalex.org/W2533129183","https://openalex.org/W2742556866","https://openalex.org/W2765461921","https://openalex.org/W2986071152","https://openalex.org/W3110051719","https://openalex.org/W4212906384","https://openalex.org/W6677790645","https://openalex.org/W6680437723","https://openalex.org/W6787343811"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2545348020","https://openalex.org/W2061955552"],"abstract_inverted_index":{"Various":[0],"landscape":[1],"characteristics":[2],"and":[3,22,39,89,104],"irregular":[4],"object":[5,9],"boundaries":[6,126],"often":[7],"make":[8],"extraction":[10,61],"more":[11,141],"difficult.":[12],"Automated":[13],"analysis":[14,67],"of":[15,45,59,111],"remote":[16,69],"sensing":[17,70],"(RS)":[18],"images":[19,71,117],"is":[20,25,72,100],"challenging":[21],"saliency":[23,76,106,120,143],"detection":[24],"an":[26,46],"effective":[27],"solution.":[28],"Yet,":[29],"many":[30],"traditional":[31],"algorithms":[32],"emphasize":[33],"simply":[34],"on":[35,64,102],"a":[36,57,94],"single":[37],"image":[38,47],"would,":[40],"therefore,":[41],"neglect":[42],"the":[43,53,79,97],"similarity":[44],"set.":[48],"In":[49],"this":[50],"paper,":[51],"concerning":[52],"relationships":[54],"among":[55],"images,":[56],"region":[58],"interest":[60,112],"model":[62],"based":[63,101],"common":[65,80],"features":[66],"for":[68],"proposed.":[73],"Firstly,":[74],"multi-image":[75],"maps,":[77],"showing":[78],"salient":[81,98],"objects,":[82],"are":[83,113],"generated":[84],"by":[85,128],"clustering":[86],"in":[87],"RGB":[88],"CIELab":[90],"color":[91],"spaces.":[92],"Next,":[93],"method,":[95],"highlighting":[96],"region,":[99],"global":[103],"local":[105],"statistics":[107],"analysis.":[108],"Finally,":[109],"regions":[110],"segmented":[114],"from":[115],"original":[116],"according":[118],"to":[119],"maps":[121],"which":[122],"have":[123],"been":[124],"made":[125],"holding":[127],"superpixels.":[129],"Experimental":[130],"evaluation":[131],"shows":[132],"that":[133],"compared":[134],"with":[135],"six":[136],"existing":[137],"models,":[138],"we":[139],"get":[140],"accurate":[142],"maps.":[144]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
