{"id":"https://openalex.org/W4312305606","doi":"https://doi.org/10.1109/igarss46834.2022.9883178","title":"Pointshift: Point-Wise Shift MLP for Pixel-Level Cloud Type Classification in Meteorological Satellite Imagery","display_name":"Pointshift: Point-Wise Shift MLP for Pixel-Level Cloud Type Classification in Meteorological Satellite Imagery","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312305606","doi":"https://doi.org/10.1109/igarss46834.2022.9883178"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9883178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883178","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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/A5100755268","display_name":"Yixiang Huang","orcid":"https://orcid.org/0000-0003-0809-7890"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yixiang Huang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102004334","display_name":"Zhaoqing Wang","orcid":"https://orcid.org/0000-0001-9900-4287"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]},{"id":"https://openalex.org/I2799867020","display_name":"Darlington College","ror":"https://ror.org/055khxa55","country_code":"GB","type":"education","lineage":["https://openalex.org/I2799867020"]}],"countries":["AU","GB"],"is_corresponding":false,"raw_author_name":"Zhaoqing Wang","raw_affiliation_strings":["School of Computer Science, University of Sydney,Darlington,Australia","School of Computer Science, University of Sydney, Darlington, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Sydney,Darlington,Australia","institution_ids":["https://openalex.org/I2799867020","https://openalex.org/I129604602"]},{"raw_affiliation_string":"School of Computer Science, University of Sydney, Darlington, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101541733","display_name":"Xin Jiang","orcid":"https://orcid.org/0000-0003-4141-1538"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Jiang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109593684","display_name":"Ming Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Wu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015730388","display_name":"Chuang Zhang","orcid":"https://orcid.org/0000-0002-1115-5580"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Zhang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100445470","display_name":"Jun Guo","orcid":"https://orcid.org/0000-0001-9045-1339"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Guo","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100755268"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.23712297,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"607","last_page":"610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9972000122070312,"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":0.9972000122070312,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9961000084877014,"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":0.7796105146408081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5943086743354797},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5685924291610718},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5465781688690186},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4698159992694855},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.46874454617500305},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44820502400398254},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4423685371875763},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.43794649839401245},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4277368187904358},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4237639904022217},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4211121201515198},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41652220487594604},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4119637906551361},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07407879829406738},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07035917043685913}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7796105146408081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5943086743354797},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5685924291610718},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5465781688690186},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4698159992694855},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.46874454617500305},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44820502400398254},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4423685371875763},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.43794649839401245},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4277368187904358},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4237639904022217},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4211121201515198},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41652220487594604},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4119637906551361},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07407879829406738},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07035917043685913},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9883178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883178","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.8399999737739563,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2889664744","https://openalex.org/W2964309882","https://openalex.org/W3160284783","https://openalex.org/W3189759539","https://openalex.org/W3205180329","https://openalex.org/W4297665946","https://openalex.org/W6639824700","https://openalex.org/W6748481559","https://openalex.org/W6775845032","https://openalex.org/W6795435739","https://openalex.org/W6799334307"],"related_works":["https://openalex.org/W3016928466","https://openalex.org/W4389574804","https://openalex.org/W3135697610","https://openalex.org/W4375867731","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2936725271","https://openalex.org/W2171299904","https://openalex.org/W3150655618","https://openalex.org/W3108295644"],"abstract_inverted_index":{"The":[0],"deep":[1],"neural":[2],"network":[3],"has":[4],"recently":[5],"achieved":[6],"promising":[7],"results":[8],"on":[9,106],"cloud":[10,64],"type":[11,65],"classification,":[12,66],"which":[13,43,69],"gets":[14],"rid":[15],"of":[16,40,101,120],"the":[17,38,41,99,107,117],"hand-crafted":[18],"features":[19],"and":[20,73,122,128],"plays":[21],"an":[22],"essential":[23],"role":[24],"in":[25,88],"climate":[26],"change":[27],"analysis.":[28],"Previous":[29],"methods":[30],"perform":[31],"context":[32],"reasoning":[33],"with":[34],"single-scale":[35],"representation":[36],"at":[37],"centre":[39],"network,":[42],"is":[44],"challenging":[45],"to":[46,82,97],"capture":[47],"sufficient":[48],"contextual":[49],"information.":[50,76],"In":[51],"this":[52],"paper,":[53],"we":[54,93],"propose":[55],"a":[56,79,84,89,123],"point-wise":[57,72],"shift":[58,80],"multi-layer":[59],"perceptron":[60],"(MLP)":[61],"for":[62],"pixel-level":[63],"termed":[67],"PointShift,":[68],"effectively":[70],"models":[71],"multi-scale":[74,85],"neighbour":[75],"We":[77],"design":[78],"operation":[81],"compose":[83],"receptive":[86],"field":[87],"non-parametric":[90],"manner.":[91],"Besides,":[92],"introduce":[94],"split":[95],"attention":[96],"improve":[98],"interaction":[100],"feature":[102],"channels.":[103],"Extensive":[104],"experiments":[105],"Himawari-8":[108],"image":[109],"dataset":[110],"demonstrate":[111],"that":[112],"our":[113],"proposed":[114],"architecture":[115],"achieves":[116],"best":[118],"mIoU":[119],"71.06%":[121],"competitive":[124],"trade-off":[125],"between":[126],"efficiency":[127],"performance.":[129]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}