{"id":"https://openalex.org/W4384080234","doi":"https://doi.org/10.1109/tgrs.2023.3294817","title":"Leveraging Physical Rules for Weakly Supervised Cloud Detection in Remote Sensing Images","display_name":"Leveraging Physical Rules for Weakly Supervised Cloud Detection in Remote Sensing Images","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4384080234","doi":"https://doi.org/10.1109/tgrs.2023.3294817"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3294817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3294817","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5020484951","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0003-0010-5200"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0010-5200","affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005350496","display_name":"Qingyong Li","orcid":"https://orcid.org/0000-0002-3860-4809"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyong Li","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3860-4809","affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101617347","display_name":"Xiaobao Li","orcid":"https://orcid.org/0000-0002-4088-0060"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaobao Li","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021115301","display_name":"Shuyi He","orcid":"https://orcid.org/0000-0002-8427-4089"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyi He","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8427-4089","affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054864219","display_name":"Fengjiao Liang","orcid":"https://orcid.org/0000-0003-3845-8781"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengjiao Liang","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037675566","display_name":"Zhigang Yao","orcid":"https://orcid.org/0000-0001-9277-1715"},"institutions":[{"id":"https://openalex.org/I4210137516","display_name":"Beijing Meteorological Bureau","ror":"https://ror.org/03wd9vk87","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210137516"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Yao","raw_affiliation_strings":["Beijing Institute of Applied Meteorology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Applied Meteorology, Beijing, China","institution_ids":["https://openalex.org/I4210137516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101933866","display_name":"Jun Jiang","orcid":"https://orcid.org/0000-0002-6351-1129"},"institutions":[{"id":"https://openalex.org/I4210137516","display_name":"Beijing Meteorological Bureau","ror":"https://ror.org/03wd9vk87","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210137516"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Jiang","raw_affiliation_strings":["Beijing Institute of Applied Meteorology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Applied Meteorology, Beijing, China","institution_ids":["https://openalex.org/I4210137516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100396367","display_name":"Wen Wang","orcid":"https://orcid.org/0000-0002-6076-9714"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Wang","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6076-9714","affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1503,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80902993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9993000030517578,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9990000128746033,"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":0.8623306155204773},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.7496472597122192},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6100785136222839},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6007790565490723},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.518832802772522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4804255962371826},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4337097406387329},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4333209693431854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42238831520080566},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3824479579925537}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8623306155204773},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7496472597122192},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6100785136222839},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6007790565490723},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.518832802772522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4804255962371826},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4337097406387329},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4333209693431854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42238831520080566},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3824479579925537},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3294817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3294817","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1170972663","display_name":null,"funder_award_id":"2023JBZY037","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G1375215039","display_name":null,"funder_award_id":"62276019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3445688267","display_name":null,"funder_award_id":"2022JBQY007","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7425124260","display_name":null,"funder_award_id":"62006017","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/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W625827677","https://openalex.org/W1533693043","https://openalex.org/W1686810756","https://openalex.org/W1967386275","https://openalex.org/W2001599060","https://openalex.org/W2014096274","https://openalex.org/W2025745000","https://openalex.org/W2028191110","https://openalex.org/W2028240797","https://openalex.org/W2068124105","https://openalex.org/W2133059825","https://openalex.org/W2163605009","https://openalex.org/W2166251851","https://openalex.org/W2194775991","https://openalex.org/W2326555679","https://openalex.org/W2412782625","https://openalex.org/W2565639579","https://openalex.org/W2584156879","https://openalex.org/W2601564443","https://openalex.org/W2605847660","https://openalex.org/W2788309473","https://openalex.org/W2801541271","https://openalex.org/W2886928598","https://openalex.org/W2901249518","https://openalex.org/W2904005494","https://openalex.org/W2914757358","https://openalex.org/W2927122915","https://openalex.org/W2946072066","https://openalex.org/W2950314938","https://openalex.org/W2964334477","https://openalex.org/W2982092116","https://openalex.org/W2989491750","https://openalex.org/W3000627240","https://openalex.org/W3027738884","https://openalex.org/W3028160024","https://openalex.org/W3037529526","https://openalex.org/W3042088791","https://openalex.org/W3080368312","https://openalex.org/W3098834274","https://openalex.org/W3110908156","https://openalex.org/W3118957360","https://openalex.org/W3129390331","https://openalex.org/W3132505321","https://openalex.org/W3134927667","https://openalex.org/W3138074985","https://openalex.org/W3173374491","https://openalex.org/W3183732083","https://openalex.org/W3184748435","https://openalex.org/W3192168220","https://openalex.org/W3194376449","https://openalex.org/W3194482819","https://openalex.org/W3203755984","https://openalex.org/W4205510142","https://openalex.org/W4206571460","https://openalex.org/W4210847275","https://openalex.org/W4213186602","https://openalex.org/W4220917047","https://openalex.org/W4224028831","https://openalex.org/W4285198998","https://openalex.org/W4285207472","https://openalex.org/W4285302592","https://openalex.org/W4289205113","https://openalex.org/W4292702164","https://openalex.org/W4313192742","https://openalex.org/W4313438466","https://openalex.org/W4366310757","https://openalex.org/W4376481071","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6809373505"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4285411112","https://openalex.org/W2085033728","https://openalex.org/W2171299904","https://openalex.org/W4390494008","https://openalex.org/W2922442631","https://openalex.org/W2053596378","https://openalex.org/W2168523118","https://openalex.org/W4315434538","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Cloud":[0],"detection":[1,15,38,50,125],"plays":[2],"a":[3,34,56,142,166],"significant":[4],"role":[5],"in":[6,51,210],"remote":[7,52],"sensing":[8,53],"image":[9],"applications.":[10],"Existing":[11],"deep":[12],"learning-based":[13],"cloud":[14,37,49,69,73,98,124,163],"methods":[16],"rely":[17],"on":[18,72,187],"massive":[19],"precise":[20],"pixel-wise":[21,128],"annotations,":[22],"which":[23],"are":[24,115],"time-consuming":[25],"and":[26,92,99,107,138,157,178,192],"expensive.":[27],"To":[28],"alleviate":[29],"this":[30,208],"problem,":[31],"we":[32],"propose":[33],"weakly":[35],"supervised":[36],"framework":[39],"that":[40,131,196],"leverages":[41],"physical":[42,81],"rules":[43],"to":[44,65,96,120,149,171],"generate":[45],"weak":[46,118],"supervision":[47,119],"for":[48,127],"images.":[54],"Specifically,":[55],"rule-based":[57],"adaptive":[58,93],"pseudo":[59,113],"labeling":[60],"(RAPL)":[61],"algorithm":[62],"is":[63,147,169,212],"devised":[64],"adaptively":[66],"annotate":[67,97],"potential":[68],"pixels":[70],"based":[71],"spectral":[74,140,176],"properties":[75],"without":[76],"manual":[77],"intervention.":[78],"Unlike":[79],"existing":[80],"annotations":[82],"using":[83],"fixed":[84],"thresholds,":[85],"RAPL":[86],"employs":[87],"the":[88,122,151,180,188,197],"bidirectional":[89],"threshold":[90],"segmentation":[91],"gating":[94],"mechanism":[95],"boundary":[100,144],"masks":[101,114],"with":[102,174],"more":[103],"explicit":[104],"semantic":[105],"categories":[106],"spatial":[108,155],"structures":[109,137],"separately.":[110],"Subsequently,":[111],"these":[112],"treated":[116],"as":[117,134],"optimize":[121],"heuristic":[123],"network":[126],"segmentation.":[129],"Considering":[130],"clouds":[132,173],"appear":[133],"complex":[135],"geometric":[136],"nonuniform":[139,175],"reflectance,":[141],"deformable":[143],"refining":[145],"module":[146],"designed":[148],"enhance":[150],"modeling":[152],"ability":[153],"of":[154,182,207],"transformation":[156],"activate":[158],"sharp":[159],"boundaries":[160],"from":[161],"translucent":[162],"regions.":[164],"Moreover,":[165],"harmonic":[167],"loss":[168],"employed":[170],"recognize":[172],"reflectance":[177],"suppress":[179],"interference":[181],"bright":[183],"backgrounds.":[184],"Extensive":[185],"experiments":[186],"GF-1,":[189],"L8":[190],"Biome,":[191],"WDCD":[193],"datasets":[194],"demonstrate":[195],"proposed":[198],"method":[199],"achieves":[200],"state-of-the-art":[201],"results.":[202],"A":[203],"public":[204],"reference":[205],"implementation":[206],"work":[209],"PyTorch":[211],"available":[213],"at":[214],"https://github.com/NiAn-creator/HeuristicCloudDetection.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
