{"id":"https://openalex.org/W4285106080","doi":"https://doi.org/10.1109/tgrs.2022.3181501","title":"S3Net: Spectral\u2013Spatial Siamese Network for Few-Shot Hyperspectral Image Classification","display_name":"S3Net: Spectral\u2013Spatial Siamese Network for Few-Shot Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285106080","doi":"https://doi.org/10.1109/tgrs.2022.3181501"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3181501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3181501","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5107976561","display_name":"Zhaohui Xue","orcid":"https://orcid.org/0000-0002-1672-317X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Xue","raw_affiliation_strings":["School of Earth Sciences and Engineering and the Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1672-317X","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering and the Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054585852","display_name":"Yiyang Zhou","orcid":"https://orcid.org/0000-0002-2888-830X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyang Zhou","raw_affiliation_strings":["School of Earth Sciences and Engineering and the Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2888-830X","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering and the Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100690406","display_name":"Peijun Du","orcid":"https://orcid.org/0000-0002-2488-2656"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210141657","display_name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application","ror":"https://ror.org/045yewh40","country_code":"CN","type":"facility","lineage":["https://openalex.org/I152031979","https://openalex.org/I4210141657"]},{"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":"Peijun Du","raw_affiliation_strings":["Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, the Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, and the School of Geography and Ocean Science, Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2488-2656","affiliations":[{"raw_affiliation_string":"Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, the Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, and the School of Geography and Ocean Science, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I211433327","https://openalex.org/I4210141657","https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.2228,"has_fulltext":false,"cited_by_count":109,"citation_normalized_percentile":{"value":0.98405322,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9883000254631042,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9807000160217285,"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/overfitting","display_name":"Overfitting","score":0.8031328916549683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7553333044052124},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7399349212646484},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7360207438468933},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7094274163246155},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5460259914398193},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.5162946581840515},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4921242594718933},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4473777711391449},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4296899437904358},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30875417590141296},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2718038558959961}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8031328916549683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7553333044052124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7399349212646484},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7360207438468933},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7094274163246155},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5460259914398193},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.5162946581840515},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4921242594718933},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4473777711391449},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4296899437904358},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30875417590141296},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2718038558959961},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3181501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3181501","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6200000047683716}],"awards":[{"id":"https://openalex.org/G179363741","display_name":"\u9ad8\u5149\u8c31\u9065\u611f\u5f71\u50cf\u534a\u76d1\u7763\u6df1\u5ea6\u5b66\u4e60\u4e0e\u5206\u7c7b\u7814\u7a76","funder_award_id":"41971279","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8870202964","display_name":null,"funder_award_id":"B200202012","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1522301498","https://openalex.org/W1930624869","https://openalex.org/W1950365613","https://openalex.org/W2029316659","https://openalex.org/W2069231830","https://openalex.org/W2090424610","https://openalex.org/W2412588858","https://openalex.org/W2464755555","https://openalex.org/W2500751094","https://openalex.org/W2602024454","https://openalex.org/W2743255627","https://openalex.org/W2764034829","https://openalex.org/W2764276316","https://openalex.org/W2767805377","https://openalex.org/W2782517596","https://openalex.org/W2782522152","https://openalex.org/W2800371750","https://openalex.org/W2894165434","https://openalex.org/W2898204262","https://openalex.org/W2909158354","https://openalex.org/W2940678725","https://openalex.org/W2942454403","https://openalex.org/W2947471972","https://openalex.org/W2949846184","https://openalex.org/W2962770389","https://openalex.org/W2991616716","https://openalex.org/W2998247074","https://openalex.org/W3006703771","https://openalex.org/W3011495011","https://openalex.org/W3012405452","https://openalex.org/W3025582806","https://openalex.org/W3034942609","https://openalex.org/W3036990306","https://openalex.org/W3064134516","https://openalex.org/W3096751897","https://openalex.org/W3098388691","https://openalex.org/W3105492824","https://openalex.org/W3120356488","https://openalex.org/W3124294118","https://openalex.org/W3132867842","https://openalex.org/W3138725786","https://openalex.org/W3169327214","https://openalex.org/W3189063576","https://openalex.org/W3197866960","https://openalex.org/W3205614732","https://openalex.org/W4239510810","https://openalex.org/W4240485910","https://openalex.org/W4382897218","https://openalex.org/W6631190155","https://openalex.org/W6729983426"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W3128011703","https://openalex.org/W4387800741"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"(DL)":[2],"has":[3],"shown":[4],"great":[5],"potentials":[6],"for":[7,61,115],"hyperspectral":[8],"image":[9],"(HSI)":[10],"classification":[11,47,197],"due":[12,32],"to":[13,33,79,131,141,145,159,170],"its":[14],"powerful":[15],"ability":[16],"of":[17,72,175,220],"nonlinear":[18],"modeling":[19],"and":[20,43,74,105,193,209],"end-to-end":[21],"optimization.":[22],"However,":[23],"DL":[24],"models":[25],"are":[26,120,128,157],"easily":[27],"get":[28],"trapped":[29],"into":[30,102,122],"overfitting":[31],"limited":[34],"training":[35,96,201],"labels":[36],"since":[37],"the":[38,108,116,143,148,172,176,205,210,217,221],"labeling":[39],"process":[40],"is":[41,77,85,139,168],"time-consuming":[42],"laborious":[44],"in":[45,90,147],"real":[46],"scenario.":[48,202],"To":[49,111],"overcome":[50],"this":[51],"issue,":[52],"we":[53],"propose":[54],"a":[55,66,135],"novel":[56],"spectral-spatial":[57,68,81],"siamese":[58],"network":[59,69],"(S3Net)":[60],"few-shot":[62,200],"HSI":[63,185,196],"classification.":[64],"Firstly,":[65],"lightweight":[67],"(SSN)":[70],"composed":[71],"1-D":[73],"2-D":[75],"convolution":[76],"proposed":[78],"extract":[80],"features.":[82],"Secondly,":[83],"S3Net":[84,190],"constructed":[86],"by":[87,98,151],"two":[88,177],"SSNs":[89],"dual":[91],"branches,":[92],"which":[93],"can":[94],"augment":[95],"set":[97],"feeding":[99],"sample":[100,154],"pairs":[101,155],"each":[103,123],"branch,":[104,124],"thus":[106],"enhancing":[107],"model":[109,144],"separability.":[110],"provide":[112],"more":[113],"features":[114],"model,":[117],"differentiated":[118],"patches":[119],"fed":[121],"where":[125],"negative":[126],"samples":[127],"random":[129],"selected":[130],"avoid":[132],"redundancy.":[133],"Finally,":[134],"weighted":[136,206],"contrastive":[137,207],"loss":[138,167,208,214],"designed":[140],"promote":[142],"fit":[146],"right":[149],"direction":[150],"focusing":[152],"on":[153,181],"that":[156,189],"hardly":[158],"be":[160],"identified.":[161],"Moreover,":[162],"another":[163],"adaptive":[164,211],"cross":[165,212],"entropy":[166,213],"conceived":[169],"learn":[171],"fusion":[173],"ratio":[174],"branches.":[178],"Experiments":[179],"based":[180],"three":[182],"commonly":[183],"used":[184],"data":[186],"sets":[187],"demonstrate":[188],"outperforms":[191],"traditional":[192],"state-of-the-art":[194],"DL-based":[195],"methods":[198],"under":[199],"In":[203],"addition,":[204],"jointly":[215],"improve":[216],"discrimination":[218],"power":[219],"model.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":32},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}