{"id":"https://openalex.org/W4402259439","doi":"https://doi.org/10.1109/igarss53475.2024.10642790","title":"Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer","display_name":"Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402259439","doi":"https://doi.org/10.1109/igarss53475.2024.10642790"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10642790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss53475.2024.10642790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 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/A5092200154","display_name":"Pakezhamu Nuradili","orcid":"https://orcid.org/0000-0002-5585-1377"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pakezhamu Nuradili","raw_affiliation_strings":["University of Electronic Science and Technology,School of Resources and Environment,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology,School of Resources and Environment,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076740062","display_name":"Ji Zhou","orcid":"https://orcid.org/0000-0001-9926-7693"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Zhou","raw_affiliation_strings":["University of Electronic Science and Technology,School of Resources and Environment,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology,School of Resources and Environment,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021389231","display_name":"Farid Melgani","orcid":"https://orcid.org/0000-0001-9745-3732"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Farid Melgani","raw_affiliation_strings":["University of Trento,Dept. of Information Engineering and Computer Science,Trento,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Trento,Dept. of Information Engineering and Computer Science,Trento,Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5092200154"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.5989,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66691,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"6576","last_page":"6579"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9435999989509583,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9435999989509583,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9276000261306763,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9258000254631042,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.8417848348617554},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.7208885550498962},{"id":"https://openalex.org/keywords/wetland","display_name":"Wetland","score":0.7126196026802063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6468644142150879},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6068691611289978},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5801353454589844},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5455949902534485},{"id":"https://openalex.org/keywords/multispectral-pattern-recognition","display_name":"Multispectral pattern recognition","score":0.5226414203643799},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4974658787250519},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4331982731819153},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.33660978078842163},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.24891895055770874}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8417848348617554},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.7208885550498962},{"id":"https://openalex.org/C67715294","wikidata":"https://www.wikidata.org/wiki/Q170321","display_name":"Wetland","level":2,"score":0.7126196026802063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6468644142150879},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6068691611289978},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5801353454589844},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5455949902534485},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.5226414203643799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4974658787250519},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4331982731819153},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.33660978078842163},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.24891895055770874},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss53475.2024.10642790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss53475.2024.10642790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.unitn.it:11572/444770","is_oa":false,"landing_page_url":"https://hdl.handle.net/11572/444770","pdf_url":null,"source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2110764733","https://openalex.org/W2770233088","https://openalex.org/W3094502228","https://openalex.org/W3100339464","https://openalex.org/W3202066404","https://openalex.org/W3211490618","https://openalex.org/W4293411918","https://openalex.org/W6784333009","https://openalex.org/W6797399245"],"related_works":["https://openalex.org/W2128126485","https://openalex.org/W4382563209","https://openalex.org/W2124952510","https://openalex.org/W2777937183","https://openalex.org/W2108633818","https://openalex.org/W1752760603","https://openalex.org/W1995889410","https://openalex.org/W4389779246","https://openalex.org/W1517037119","https://openalex.org/W2125510194"],"abstract_inverted_index":{"In":[0,52,105],"this":[1],"study,":[2],"an":[3],"end-to-end":[4,55],"semantic":[5,58,72],"segmentation":[6,47,59,107,137],"method":[7,60,128],"(ConvSegFormer)":[8],"is":[9,81,129],"proposed":[10],"by":[11],"utilizing":[12],"the":[13,36,54,64,87,106,110,113,127,136],"multispectral":[14,22,39],"imaging":[15],"capability":[16],"of":[17,38,90,109,116,138],"UAVs":[18],"for":[19],"images":[20],"containing":[21],"bands,":[23,44],"with":[24],"a":[25],"special":[26],"focus":[27],"on":[28],"thermal":[29,42],"infrared":[30,43],"bands.":[31],"Experimental":[32],"results":[33],"show":[34],"that":[35,126],"use":[37],"images,":[40],"especially":[41],"achieves":[45],"higher":[46],"accuracy":[48],"through":[49],"spectral":[50],"information.":[51],"addition,":[53],"deep":[56],"learning":[57],"can":[61],"directly":[62],"learn":[63],"complex":[65],"mapping":[66],"relationship":[67],"between":[68],"image":[69],"pixels":[70],"and":[71,78,84,95,103,118,122,131,140],"categories":[73],"without":[74],"step-by-step":[75],"feature":[76],"extraction":[77],"classification,":[79],"which":[80],"more":[82],"direct":[83],"efficient.":[85],"Finally,":[86],"maximum":[88,114],"values":[89,115],"Mean":[91,96],"Pixel":[92],"Accuracy":[93],"(MPA)":[94],"Intersection":[97],"Over":[98],"Union":[99],"(MIOU)":[100],"are":[101],"90.35%":[102],"73.87%.":[104],"task":[108],"wetland":[111],"area,":[112],"PA":[117],"IOU":[119],"reached":[120],"95.42%":[121],"90.46%.":[123],"This":[124],"indicates":[125],"effective":[130],"feasible":[132],"in":[133],"automatically":[134],"extracting":[135],"wetlands":[139],"other":[141],"land":[142],"types.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-03T09:05:47.796612","created_date":"2025-10-10T00:00:00"}