{"id":"https://openalex.org/W7160249307","doi":"https://doi.org/10.1109/wacv61042.2026.00730","title":"Remote Sensing Forestry Similarity Convolution","display_name":"Remote Sensing Forestry Similarity Convolution","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160249307","doi":"https://doi.org/10.1109/wacv61042.2026.00730"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00730","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5058410974","display_name":"Shikuan Wang","orcid":"https://orcid.org/0000-0003-2780-9590"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shikuan Wang","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041316452","display_name":"Yuangong Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yuangong Chen","raw_affiliation_strings":["The Hong Kong Polytechnic University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033683674","display_name":"Jianzhou Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzhou Gong","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135305105","display_name":"Lingyi Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I182707071","display_name":"Ludong University","ror":"https://ror.org/028h95t32","country_code":"CN","type":"education","lineage":["https://openalex.org/I182707071"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyi Meng","raw_affiliation_strings":["Ludong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ludong University","institution_ids":["https://openalex.org/I182707071"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004446933","display_name":"Mengquan Wu","orcid":"https://orcid.org/0000-0003-1822-5980"},"institutions":[{"id":"https://openalex.org/I182707071","display_name":"Ludong University","ror":"https://ror.org/028h95t32","country_code":"CN","type":"education","lineage":["https://openalex.org/I182707071"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengquan Wu","raw_affiliation_strings":["Ludong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ludong University","institution_ids":["https://openalex.org/I182707071"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064823133","display_name":"L. Liu","orcid":"https://orcid.org/0000-0002-6508-6496"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longxing Liu","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079578589","display_name":"Haiwei Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiwei Yuan","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135385710","display_name":"Mingbin Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingbin Guo","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7066533,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7565","last_page":"7575"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.16680000722408295,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.16680000722408295,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.1525000035762787,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.13289999961853027,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6312999725341797},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5795999765396118},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5638999938964844},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5509999990463257},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5339999794960022},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5291000008583069},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4471000134944916},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.4000999927520752}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7268000245094299},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6434000134468079},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6312999725341797},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5795999765396118},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5638999938964844},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5509999990463257},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5291000008583069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4880000054836273},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4471000134944916},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4399000108242035},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.4000999927520752},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3813000023365021},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3571000099182129},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.25870001316070557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00730","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00730","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":48,"referenced_works":["https://openalex.org/W1486317198","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2601564443","https://openalex.org/W2799213142","https://openalex.org/W2807974043","https://openalex.org/W2883105896","https://openalex.org/W2884822772","https://openalex.org/W2903995737","https://openalex.org/W2909177355","https://openalex.org/W2910628332","https://openalex.org/W2963294354","https://openalex.org/W2963881378","https://openalex.org/W2963890956","https://openalex.org/W2981689412","https://openalex.org/W2982220924","https://openalex.org/W2985459778","https://openalex.org/W3034421924","https://openalex.org/W3109998321","https://openalex.org/W3112503277","https://openalex.org/W3124539583","https://openalex.org/W3160694286","https://openalex.org/W3200075728","https://openalex.org/W3210465753","https://openalex.org/W4205487499","https://openalex.org/W4229058281","https://openalex.org/W4285795007","https://openalex.org/W4303980643","https://openalex.org/W4308970191","https://openalex.org/W4321021048","https://openalex.org/W4367016725","https://openalex.org/W4384207671","https://openalex.org/W4385859309","https://openalex.org/W4386604904","https://openalex.org/W4386634500","https://openalex.org/W4386803498","https://openalex.org/W4387618217","https://openalex.org/W4390671094","https://openalex.org/W4393999141","https://openalex.org/W4399894838","https://openalex.org/W4400579361","https://openalex.org/W4400859202","https://openalex.org/W4401567666","https://openalex.org/W4403531936","https://openalex.org/W4405270478","https://openalex.org/W4405675957","https://openalex.org/W4406487831","https://openalex.org/W4407948310"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,24,128,135],"convolutional":[3],"neural":[4],"networks":[5],"(CNNs)":[6],"have":[7],"significantly":[8],"propelled":[9],"the":[10,47,74,79,86,94],"field":[11,76],"of":[12,82,88,130],"remote":[13,119],"sensing":[14,120],"forestry":[15,89],"mapping.":[16],"However,":[17],"traditional":[18],"convolution":[19,63],"operations":[20],"exhibit":[21],"inherent":[22],"limitations":[23],"extracting":[25],"complex":[26],"forest":[27,37],"features:":[28],"their":[29,40],"fixed":[30],"receptive":[31,75],"fields":[32],"struggle":[33],"to":[34],"accommodate":[35],"multi-scale":[36],"attributes,":[38],"and":[39,92],"insufficient":[41],"focus":[42],"on":[43,78],"background":[44,90],"information":[45,91],"impairs":[46],"overall":[48],"feature":[49,69,106,131],"representation.":[50],"To":[51],"address":[52],"these":[53],"challenges,":[54],"we":[55,102],"propose":[56],"Similar":[57],"Convolution":[58],"(SimConv),":[59],"which":[60],"introduces":[61],"dynamic":[62],"kernel":[64],"size":[65],"selection":[66],"by":[67],"modeling":[68],"relationships.":[70],"SimConv":[71,111],"adaptively":[72],"adjusts":[73],"based":[77],"semantic":[80],"relevance":[81],"input":[83],"features,":[84],"enhancing":[85],"capture":[87],"improving":[93],"distinction":[95],"between":[96],"target":[97],"features.":[98],"Building":[99],"upon":[100],"this,":[101],"introduce":[103],"SIMNet,":[104],"a":[105],"extraction":[107,132],"network":[108],"that":[109,123],"integrates":[110],"at":[112],"its":[113],"core.":[114],"Experimental":[115],"results":[116],"across":[117],"multiple":[118],"datasets":[121],"demonstrate":[122],"SIMNet":[124],"outperforms":[125],"existing":[126],"methods":[127],"terms":[129],"accuracy.":[133],"code":[134],"https://github.com/WangShiK/SimConv.":[136]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}
