{"id":"https://openalex.org/W4316040044","doi":"https://doi.org/10.3390/rs15020470","title":"Unsupervised SAR Image Change Detection Based on Histogram Fitting Error Minimization and Convolutional Neural Network","display_name":"Unsupervised SAR Image Change Detection Based on Histogram Fitting Error Minimization and Convolutional Neural Network","publication_year":2023,"publication_date":"2023-01-13","ids":{"openalex":"https://openalex.org/W4316040044","doi":"https://doi.org/10.3390/rs15020470"},"language":"en","primary_location":{"id":"doi:10.3390/rs15020470","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020470","pdf_url":"https://www.mdpi.com/2072-4292/15/2/470/pdf?version=1673592080","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/2/470/pdf?version=1673592080","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005219455","display_name":"Kaiyu Zhang","orcid":"https://orcid.org/0000-0002-2413-6370"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiyu Zhang","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China","Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China","School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072466026","display_name":"Xiaolei Lv","orcid":"https://orcid.org/0000-0001-6991-583X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaolei Lv","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China","Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China","School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"],"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078005520","display_name":"Bin Guo","orcid":"https://orcid.org/0000-0001-6097-2467"},"institutions":[{"id":"https://openalex.org/I4210162399","display_name":"Airport Shunyi District Hospital","ror":"https://ror.org/053ws4838","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210162399"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Guo","raw_affiliation_strings":["Beijing Capital International Airport Group, Beijing Daxing International Airport, Beijing 102604, China"],"affiliations":[{"raw_affiliation_string":"Beijing Capital International Airport Group, Beijing Daxing International Airport, Beijing 102604, China","institution_ids":["https://openalex.org/I4210162399"]}]},{"author_position":"last","author":{"id":null,"display_name":"Huiming Chai","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiming Chai","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China","Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"],"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5072466026"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199","https://openalex.org/I4210165038"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.4707,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65639301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"15","issue":"2","first_page":"470","last_page":"470"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7614980936050415},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.7071716785430908},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6952416896820068},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6665077805519104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6498396992683411},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6304236650466919},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5950675010681152},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5704932808876038},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5553919076919556},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46573036909103394},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.4391842484474182},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4192812740802765},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.41621676087379456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7614980936050415},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.7071716785430908},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6952416896820068},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6665077805519104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6498396992683411},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6304236650466919},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5950675010681152},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5704932808876038},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5553919076919556},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46573036909103394},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.4391842484474182},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4192812740802765},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.41621676087379456},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15020470","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020470","pdf_url":"https://www.mdpi.com/2072-4292/15/2/470/pdf?version=1673592080","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f64e4ad4fc8c4cdab080464e63971943","is_oa":true,"landing_page_url":"https://doaj.org/article/f64e4ad4fc8c4cdab080464e63971943","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 2, p 470 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/2/470/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15020470","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 15; Issue 2; Pages: 470","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15020470","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020470","pdf_url":"https://www.mdpi.com/2072-4292/15/2/470/pdf?version=1673592080","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.5099999904632568}],"awards":[{"id":"https://openalex.org/G7003847482","display_name":null,"funder_award_id":"E0H2080702","funder_id":"https://openalex.org/F4320328872","funder_display_name":"China Academy of Railway Sciences"},{"id":"https://openalex.org/G8375913449","display_name":null,"funder_award_id":"2019YJ028","funder_id":"https://openalex.org/F4320328872","funder_display_name":"China Academy of Railway Sciences"}],"funders":[{"id":"https://openalex.org/F4320328872","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4316040044.pdf"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1963994432","https://openalex.org/W1965126626","https://openalex.org/W2027091505","https://openalex.org/W2028151808","https://openalex.org/W2034073840","https://openalex.org/W2063145744","https://openalex.org/W2106052468","https://openalex.org/W2110519070","https://openalex.org/W2133059825","https://openalex.org/W2152185773","https://openalex.org/W2160544350","https://openalex.org/W2194775991","https://openalex.org/W2531619007","https://openalex.org/W2557628340","https://openalex.org/W2560023338","https://openalex.org/W2563613133","https://openalex.org/W2564658196","https://openalex.org/W2615543373","https://openalex.org/W2626060251","https://openalex.org/W2746791238","https://openalex.org/W2759938573","https://openalex.org/W2789781087","https://openalex.org/W2789944120","https://openalex.org/W2811044316","https://openalex.org/W2898219846","https://openalex.org/W2910587630","https://openalex.org/W2911805825","https://openalex.org/W2946535694","https://openalex.org/W2962793481","https://openalex.org/W3036616251","https://openalex.org/W3042615550","https://openalex.org/W3099193570","https://openalex.org/W3125134914","https://openalex.org/W3126702044","https://openalex.org/W3187493547","https://openalex.org/W3197715477","https://openalex.org/W3199411725","https://openalex.org/W3204854236","https://openalex.org/W3206608948","https://openalex.org/W3212894080","https://openalex.org/W4210730494","https://openalex.org/W4214928390","https://openalex.org/W4226417358","https://openalex.org/W4285410901","https://openalex.org/W6793696115","https://openalex.org/W6801106609","https://openalex.org/W6811221668","https://openalex.org/W6839942928"],"related_works":["https://openalex.org/W2122667464","https://openalex.org/W2054831422","https://openalex.org/W2106731176","https://openalex.org/W2387104004","https://openalex.org/W2164995725","https://openalex.org/W3047671631","https://openalex.org/W1548186045","https://openalex.org/W2902098370","https://openalex.org/W2147223569","https://openalex.org/W4283267580"],"abstract_inverted_index":{"Synthetic":[0],"aperture":[1],"radar":[2],"(SAR)":[3],"image":[4,24,92],"change":[5,19,44,63,186,236,293],"detection":[6,45,64,187,237,294],"is":[7,25,49,97,109,136,154,219,232,248,275,289],"one":[8],"of":[9,32,38,140,213,269,301],"the":[10,21,30,36,39,43,95,106,122,130,157,211,214,242,255,265,286],"most":[11],"important":[12],"applications":[13],"in":[14,216,254],"remote":[15],"sensing.":[16],"Before":[17],"performing":[18],"detection,":[20],"original":[22],"SAR":[23],"often":[26,41],"cropped":[27,203,229,240,263],"to":[28,51,86,163,221,233,279,297],"extract":[29],"region":[31],"interest":[33],"(ROI).":[34],"However,":[35],"size":[37],"ROI":[40],"affects":[42],"results.":[46],"Therefore,":[47,105],"it":[48],"necessary":[50],"detect":[52],"changes":[53],"using":[54,238],"local":[55,235,292],"information.":[56],"This":[57,178],"paper":[58,179,218],"proposes":[59],"a":[60,90,100,137,172],"novel":[61],"unsupervised":[62],"framework":[65],"based":[66],"on":[67,188,224,272],"deep":[68],"learning.":[69],"The":[70,132,146,191,206,227,245],"specific":[71],"method":[72,108,215,247,271,288],"steps":[73],"are":[74,193],"described":[75],"as":[76,176],"follows:":[77],"First,":[78],"we":[79],"use":[80],"histogram":[81],"fitting":[82],"error":[83],"minimization":[84],"(HFEM)":[85],"perform":[87,234],"thresholding":[88],"for":[89,129,182,291],"difference":[91],"(DI).":[93],"Then,":[94],"DI":[96],"fed":[98],"into":[99,195],"convolutional":[101,125],"neural":[102,126],"network":[103,127],"(CNN).":[104],"proposed":[107,166,246,287],"called":[110,118],"HFEM-CNN.":[111],"We":[112],"test":[113],"three":[114,189],"different":[115],"CNN":[116],"architectures":[117],"Unet,":[119],"PSPNet":[120],"and":[121,143,151,184,201,295,299],"designed":[123],"fully":[124],"(FCNN)":[128],"framework.":[131],"overall":[133],"loss":[134,142,150,153],"function":[135],"weighted":[138],"average":[139,266],"pixel":[141,149],"neighborhood":[144,152],"loss.":[145],"weight":[147],"between":[148],"determined":[155],"by":[156],"manually":[158],"set":[159],"parameter":[160],"\u03bb.":[161],"Compared":[162],"other":[164,222,280],"recently":[165],"methods,":[167],"HFEM-CNN":[168],"does":[169],"not":[170],"need":[171],"fragment":[173],"removal":[174],"procedure":[175],"post-processing.":[177],"conducts":[180],"experiments":[181,192,200,208,260],"water":[183],"building":[185],"datasets.":[190,226,244],"divided":[194],"two":[196],"parts:":[197],"whole":[198,243,256],"data":[199,204,230,257],"random":[202,228],"experiments.":[205,258],"complete":[207,225],"prove":[209],"that":[210,285],"performance":[212],"this":[217],"close":[220],"methods":[223,253],"experiment":[231],"patches":[239,274],"from":[241],"slightly":[249],"better":[250],"than":[251],"traditional":[252],"In":[259],"with":[261],"randomly":[262],"data,":[264],"kappa":[267],"coefficient":[268],"our":[270],"63":[273],"over":[276],"3.16%":[277],"compared":[278],"methods.":[281],"Experiments":[282],"also":[283],"show":[284],"suitable":[290],"robust":[296],"randomness":[298],"choice":[300],"hyperparameters.":[302]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}