{"id":"https://openalex.org/W4411551906","doi":"https://doi.org/10.1109/cscwd64889.2025.11033621","title":"Dual Attention Pathway for Semantic Segmentation of Agricultural Remote Sensing Images","display_name":"Dual Attention Pathway for Semantic Segmentation of Agricultural Remote Sensing Images","publication_year":2025,"publication_date":"2025-05-05","ids":{"openalex":"https://openalex.org/W4411551906","doi":"https://doi.org/10.1109/cscwd64889.2025.11033621"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd64889.2025.11033621","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","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/A5067065399","display_name":"Hongqi Li","orcid":"https://orcid.org/0000-0003-4898-4857"},"institutions":[{"id":"https://openalex.org/I55654194","display_name":"Inner Mongolia University of Technology","ror":"https://ror.org/05564e019","country_code":"CN","type":"education","lineage":["https://openalex.org/I55654194"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongqi Li","raw_affiliation_strings":["College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China"],"affiliations":[{"raw_affiliation_string":"College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China","institution_ids":["https://openalex.org/I55654194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101763777","display_name":"Zhiqiang Liu","orcid":"https://orcid.org/0000-0002-8057-6717"},"institutions":[{"id":"https://openalex.org/I55654194","display_name":"Inner Mongolia University of Technology","ror":"https://ror.org/05564e019","country_code":"CN","type":"education","lineage":["https://openalex.org/I55654194"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Liu","raw_affiliation_strings":["College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China"],"affiliations":[{"raw_affiliation_string":"College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China","institution_ids":["https://openalex.org/I55654194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032734064","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0002-0294-1037"},"institutions":[{"id":"https://openalex.org/I55654194","display_name":"Inner Mongolia University of Technology","ror":"https://ror.org/05564e019","country_code":"CN","type":"education","lineage":["https://openalex.org/I55654194"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China"],"affiliations":[{"raw_affiliation_string":"College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China","institution_ids":["https://openalex.org/I55654194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100431485","display_name":"Wenjuan Li","orcid":"https://orcid.org/0000-0001-5913-0596"},"institutions":[{"id":"https://openalex.org/I55654194","display_name":"Inner Mongolia University of Technology","ror":"https://ror.org/05564e019","country_code":"CN","type":"education","lineage":["https://openalex.org/I55654194"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanhanxue Li","raw_affiliation_strings":["College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China"],"affiliations":[{"raw_affiliation_string":"College of Information and Engineering, Inner Mongolia University of Technology,Hohhot,China","institution_ids":["https://openalex.org/I55654194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067065399"],"corresponding_institution_ids":["https://openalex.org/I55654194"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22135066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1670","last_page":"1675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9624999761581421,"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"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9624999761581421,"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/dual","display_name":"Dual (grammatical number)","score":0.7486226558685303},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6757552027702332},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6215232014656067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5196893811225891},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.509297251701355},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4812145531177521},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.41067782044410706},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3322141468524933},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.14336854219436646},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10723075270652771},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.06686627864837646}],"concepts":[{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.7486226558685303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6757552027702332},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6215232014656067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5196893811225891},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.509297251701355},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4812145531177521},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.41067782044410706},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3322141468524933},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.14336854219436646},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10723075270652771},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.06686627864837646},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/cscwd64889.2025.11033621","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.7200000286102295,"display_name":"Zero hunger"}],"awards":[{"id":"https://openalex.org/G502395285","display_name":null,"funder_award_id":"61962044","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2034110104","https://openalex.org/W2125637308","https://openalex.org/W2169551590","https://openalex.org/W2195004773","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2767953525","https://openalex.org/W2922509574","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W3138516171","https://openalex.org/W4318149001"],"related_works":["https://openalex.org/W2355956201","https://openalex.org/W2327874825","https://openalex.org/W2386195957","https://openalex.org/W2351852648","https://openalex.org/W2613051533","https://openalex.org/W2349774843","https://openalex.org/W2121524756","https://openalex.org/W2775541961","https://openalex.org/W2267286817","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0],"the":[1,84,107,114,127,131,138],"task":[2],"of":[3,25,92,121,148,157,168],"segmenting":[4],"farmland":[5,37,55],"from":[6],"remote":[7,56,96,193],"sensing":[8,57,97,194],"images,":[9,58,195],"challenges":[10],"often":[11],"arise":[12],"due":[13],"to":[14,87,118],"significant":[15],"intra-class":[16],"variability":[17],"and":[18,39,44,79,89,119,130,150,152,159,173,180,200],"excessive":[19],"inter-class":[20],"similarity":[21],"among":[22],"different":[23],"types":[24,38],"farmland.":[26],"Common":[27],"semantic":[28,51],"segmentation":[29,52,163,205],"methods":[30],"exhibit":[31],"certain":[32],"limitations":[33],"in":[34,95,176,186],"distinguishing":[35],"various":[36],"identifying":[40],"boundaries":[41],"between":[42,197],"fields":[43],"roads.":[45],"Against":[46],"this":[47],"backdrop,":[48],"a":[49,142,153],"novel":[50],"network":[53,140],"for":[54,76],"named":[59],"T_segformer,":[60],"is":[61],"proposed.":[62],"Methodology:":[63],"The":[64,99],"Dual-Path":[65],"High-Low":[66],"Frequency":[67],"Feature":[68],"Extraction":[69],"Module":[70],"(DPHL)":[71],"employs":[72],"dual":[73],"attention":[74,117],"mechanisms":[75],"extracting":[77],"high":[78],"low-frequency":[80],"features,":[81],"thus":[82],"enhancing":[83,202],"model's":[85,108],"ability":[86],"recognize":[88],"segment":[90],"edges":[91],"varying":[93],"categories":[94],"images.":[98],"Mixed":[100],"Gated":[101],"Linear":[102],"Feedforward":[103],"Network":[104],"(MGLFFD)":[105],"improves":[106],"feature":[109],"extraction":[110],"capability":[111],"by":[112],"boosting":[113],"feedforward":[115],"network's":[116],"understanding":[120],"input":[122],"data.":[123],"Results:":[124],"Implementations":[125],"on":[126],"LoveDA":[128],"Dataset":[129,135],"Barley":[132],"Remote":[133],"Sensing":[134],"reveal":[136],"that":[137,167],"T_segformer":[139,184],"achieves":[141],"mean":[143],"Intersection":[144],"over":[145],"Union":[146],"(MIoU)":[147],"55.1%":[149],"79.10%":[151],"Mean":[154],"Accuracy":[155],"(mAcc)":[156],"66.65%":[158],"86.10%,":[160],"respectively.":[161],"Its":[162],"performance":[164],"significantly":[165],"surpasses":[166],"RSSForm,":[169],"Segformer,":[170],"Swin-UperNet,":[171],"DeepLabV3+,":[172],"FCN":[174],"models":[175],"both":[177],"evaluation":[178],"metrics":[179],"visual":[181],"prediction":[182],"outcomes.":[183],"excels":[185],"accurately":[187],"recognizing":[188],"distinct":[189],"features":[190],"within":[191],"complex":[192],"differentiating":[196],"similar":[198],"farmlands,":[199],"ultimately":[201],"target":[203],"boundary":[204],"accuracy.":[206]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
