{"id":"https://openalex.org/W2755702288","doi":"https://doi.org/10.1109/icip.2017.8296243","title":"Efficient cloud detection in remote sensing images using edge-aware segmentation network and easy-to-hard training strategy","display_name":"Efficient cloud detection in remote sensing images using edge-aware segmentation network and easy-to-hard training strategy","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2755702288","doi":"https://doi.org/10.1109/icip.2017.8296243","mag":"2755702288"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5100614604","display_name":"Kun Yuan","orcid":"https://orcid.org/0000-0002-9826-0366"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"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":"Kun Yuan","raw_affiliation_strings":["National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","School of Computer and Control Engineering, University of Chinese Academy of Sciences","National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Computer and Control Engineering, University of Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100675867","display_name":"Gaofeng Meng","orcid":"https://orcid.org/0000-0002-7103-6321"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaofeng Meng","raw_affiliation_strings":["National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031953050","display_name":"Dongcai Cheng","orcid":"https://orcid.org/0000-0002-9201-2265"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongcai Cheng","raw_affiliation_strings":["National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110376155","display_name":"Jun Bai","orcid":"https://orcid.org/0000-0002-1408-4271"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Bai","raw_affiliation_strings":["Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040673285","display_name":"Shiming Xiang","orcid":"https://orcid.org/0000-0002-2089-9733"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiming Xiang","raw_affiliation_strings":["National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100435212","display_name":"Chunhong Pan","orcid":"https://orcid.org/0000-0001-7433-4474"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhong Pan","raw_affiliation_strings":["National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition \u201cInstitute of Automation\u201d Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"National Laboratory of Pattern Recognition \"Institute of Automation\" Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100614604"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":1.8732,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.88513679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"65"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9994000196456909,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9994000196456909,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"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":0.9979000091552734,"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/cloud-computing","display_name":"Cloud computing","score":0.8645159006118774},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7914894819259644},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6588049530982971},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5922214984893799},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5854520797729492},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5049195885658264},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4990355968475342},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.48025110363960266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4492744207382202},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44515591859817505},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.43657487630844116},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.37440574169158936},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09571278095245361},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07777905464172363},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.07456859946250916}],"concepts":[{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.8645159006118774},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7914894819259644},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6588049530982971},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5922214984893799},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5854520797729492},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5049195885658264},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4990355968475342},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.48025110363960266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4492744207382202},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44515591859817505},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43657487630844116},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.37440574169158936},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09571278095245361},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07777905464172363},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.07456859946250916},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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":27,"referenced_works":["https://openalex.org/W360623563","https://openalex.org/W1608371111","https://openalex.org/W1665214252","https://openalex.org/W1745334888","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W2055138256","https://openalex.org/W2067191022","https://openalex.org/W2084516844","https://openalex.org/W2087866348","https://openalex.org/W2096148850","https://openalex.org/W2104269704","https://openalex.org/W2118246710","https://openalex.org/W2132984949","https://openalex.org/W2133326900","https://openalex.org/W2133434696","https://openalex.org/W2136251662","https://openalex.org/W2163947121","https://openalex.org/W2168158289","https://openalex.org/W2177128308","https://openalex.org/W2249172126","https://openalex.org/W2548491386","https://openalex.org/W2949117887","https://openalex.org/W6612134913","https://openalex.org/W6637242042","https://openalex.org/W6679805309","https://openalex.org/W6690951630"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2922442631","https://openalex.org/W2067272521","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Detecting":[0],"cloud":[1,40,55,92,107,110,120,129],"regions":[2],"in":[3,94,123,143,147],"remote":[4],"sensing":[5],"image":[6],"(RSI)":[7],"is":[8,25,58,99,137,155,161],"very":[9],"challenging":[10],"yet":[11],"of":[12,78,106,151],"great":[13],"importance":[14],"to":[15,44,67,101,114,139],"meteorological":[16],"forecasting":[17],"and":[18,75,109,173],"other":[19],"RSI-related":[20],"applications.":[21],"Technically,":[22],"this":[23,81],"task":[24],"typically":[26],"implemented":[27],"as":[28],"a":[29,85,116,158],"pixel-level":[30],"segmentation.":[31],"However,":[32],"traditional":[33],"methods":[34],"based":[35,87],"on":[36,178],"handcrafted":[37],"or":[38],"low-level":[39],"features":[41],"often":[42],"fail":[43],"achieve":[45],"satisfactory":[46],"performances":[47,61],"from":[48,181],"images":[49],"with":[50],"bright":[51],"non-cloud":[52],"and/or":[53],"semitransparent":[54],"regions.":[56],"What":[57],"more,":[59],"the":[60,68,103,149,152,164,196],"could":[62],"be":[63],"further":[64],"degraded":[65],"due":[66],"ambiguous":[69],"boundaries":[70],"caused":[71],"by":[72,157],"complicated":[73],"textures":[74],"non-uniform":[76],"distribution":[77],"intensities.":[79],"In":[80],"paper,":[82],"we":[83],"propose":[84],"multi-task":[86],"deep":[88],"neural":[89],"network":[90,98,142],"for":[91,127],"detection":[93,112,118],"RSIs.":[95],"Architecturally,":[96],"our":[97,141,189],"designed":[100],"combine":[102],"two":[104],"tasks":[105],"segmentation":[108],"edge":[111],"together":[113],"encourage":[115],"better":[117],"near":[119],"boundaries,":[121],"resulting":[122],"an":[124,132,144],"end-to-end":[125],"approach":[126],"accurate":[128],"detection.":[130],"Accordingly,":[131],"efficient":[133],"sample":[134],"selection":[135],"strategy":[136],"proposed":[138],"train":[140],"easy-to-hard":[145],"manner,":[146],"which":[148],"number":[150],"selected":[153],"samples":[154,167],"governed":[156],"weight":[159],"that":[160,188],"annealed":[162],"until":[163],"entire":[165],"training":[166],"have":[168],"been":[169],"considered.":[170],"Both":[171],"visual":[172],"quantitative":[174],"comparisons":[175],"are":[176],"conducted":[177],"RSIs":[179],"collected":[180],"Google":[182],"Earth.":[183],"The":[184],"experimental":[185],"results":[186],"indicate":[187],"method":[190],"can":[191],"yield":[192],"superior":[193],"performance":[194],"over":[195],"state-of-the-art":[197],"methods.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
