{"id":"https://openalex.org/W7152415995","doi":"https://doi.org/10.48550/arxiv.2604.06844","title":"CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery","display_name":"CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7152415995","doi":"https://doi.org/10.48550/arxiv.2604.06844"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06844","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06844","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06844","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107748413","display_name":"Jiajun Yang","orcid":"https://orcid.org/0009-0007-1690-0002"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jiajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133258513","display_name":"Keyan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Keyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088611151","display_name":"Zhengxia Zou","orcid":"https://orcid.org/0000-0003-1774-552X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Zhengxia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133256394","display_name":"Zhenwei Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Zhenwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.436599999666214,"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":0.436599999666214,"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":0.18279999494552612,"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"}},{"id":"https://openalex.org/T10347","display_name":"Atmospheric aerosols and clouds","score":0.06289999932050705,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/segmentation","display_name":"Segmentation","score":0.6521000266075134},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.621999979019165},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.6180999875068665},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.512499988079071},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5120999813079834},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.435699999332428},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40209999680519104},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.3952000141143799}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7710999846458435},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6521000266075134},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.6180999875068665},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5429999828338623},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4410000145435333},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.435699999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06844","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06844","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06844","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06844","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cloud":[0],"detection":[1,18,80],"in":[2,42,71,108],"remote":[3],"sensing":[4],"imagery":[5],"is":[6,87,102,204],"a":[7,24,60,98,123,129],"fundamental,":[8],"critical,":[9],"and":[10,40,45,52,97,117,155,168,178,199],"highly":[11],"challenging":[12],"problem.":[13],"Existing":[14],"deep":[15,62],"learning-based":[16],"cloud":[17,79,166],"methods":[19],"generally":[20],"formulate":[21],"it":[22],"as":[23],"single-stage":[25,36],"pixel-wise":[26],"binary":[27],"segmentation":[28,101,192],"task":[29],"with":[30,134,137],"one":[31],"forward":[32],"pass.":[33],"However,":[34],"such":[35],"approaches":[37,189],"exhibit":[38],"ambiguity":[39,70],"uncertainty":[41,84],"thin-cloud":[43,72,95],"regions":[44],"struggle":[46],"to":[47,89,104],"accurately":[48],"handle":[49,114],"fragmented":[50,115],"clouds":[51,116],"boundary":[53,119,157,170],"details.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58,74,121],"propose":[59],"novel":[61],"learning":[63],"framework":[64],"termed":[65],"CloudMamba.":[66],"To":[67,112],"address":[68],"the":[69,92,106,141,176,184],"regions,":[73],"introduce":[75],"an":[76],"uncertainty-guided":[77],"two-stage":[78],"strategy.":[81],"An":[82],"embedded":[83],"estimation":[85],"module":[86],"proposed":[88,142,185],"automatically":[90],"quantify":[91],"confidence":[93],"of":[94,159,164],"segmentation,":[96],"second-stage":[99],"refinement":[100],"introduced":[103],"improve":[105],"accuracy":[107,193],"low-confidence":[109],"hard":[110],"regions.":[111],"better":[113],"fine-grained":[118],"details,":[120],"design":[122],"dual-scale":[124],"Mamba":[125],"network":[126],"based":[127],"on":[128,175],"CNN-Mamba":[130],"hybrid":[131],"architecture.":[132],"Compared":[133],"Transformer-based":[135],"models":[136],"quadratic":[138],"computational":[139,146],"complexity,":[140],"method":[143,186],"maintains":[144],"linear":[145],"complexity":[147],"while":[148,195],"effectively":[149],"capturing":[150],"both":[151],"large-scale":[152],"structural":[153],"characteristics":[154],"small-scale":[156],"details":[158],"clouds,":[160],"enabling":[161],"accurate":[162],"delineation":[163],"overall":[165],"morphology":[167],"precise":[169],"segmentation.":[171],"Extensive":[172],"experiments":[173],"conducted":[174],"GF1_WHU":[177],"Levir_CS":[179],"public":[180],"datasets":[181],"demonstrate":[182],"that":[183],"outperforms":[187],"existing":[188],"across":[190],"multiple":[191],"metrics,":[194],"offering":[196],"high":[197],"efficiency":[198],"process":[200],"transparency.":[201],"Our":[202],"code":[203],"available":[205],"at":[206],"https://github.com/jayoungo/CloudMamba.":[207]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-10T00:00:00"}
