{"id":"https://openalex.org/W4313316171","doi":"https://doi.org/10.1109/jstars.2022.3233125","title":"Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification","display_name":"Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-12-30","ids":{"openalex":"https://openalex.org/W4313316171","doi":"https://doi.org/10.1109/jstars.2022.3233125"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2022.3233125","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3233125","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/jstars.2022.3233125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018243278","display_name":"Zhaohui Xue","orcid":"https://orcid.org/0000-0001-6253-2967"},"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, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6253-2967","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"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/A5101960921","display_name":"Tianzhi Zhu","orcid":"https://orcid.org/0000-0002-8294-2274"},"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":"Tianzhi Zhu","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8294-2274","affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"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/I4401727007","display_name":"Hikvision (China)","ror":"https://ror.org/02jzypx27","country_code":null,"type":"company","lineage":["https://openalex.org/I4401727007"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyang Zhou","raw_affiliation_strings":["Artificial Intelligence Laboratory, Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2888-830X","affiliations":[{"raw_affiliation_string":"Artificial Intelligence Laboratory, Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, China","institution_ids":["https://openalex.org/I4401727007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016879046","display_name":"Mengxue Zhang","orcid":"https://orcid.org/0000-0002-8587-4334"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mengxue Zhang","raw_affiliation_strings":["Image and Signal Processing Group, University of Val&#x00E8;ncia, Val&#x00E8;ncia, Spain"],"raw_orcid":"https://orcid.org/0000-0002-8587-4334","affiliations":[{"raw_affiliation_string":"Image and Signal Processing Group, University of Val&#x00E8;ncia, Val&#x00E8;ncia, Spain","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":0.4389,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67050691,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"16","issue":null,"first_page":"1085","last_page":"1099"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9811000227928162,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9753999710083008,"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/interpretability","display_name":"Interpretability","score":0.9298428297042847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8093560338020325},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7159921526908875},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7141287326812744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7032257318496704},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6505112648010254},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5700714588165283},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4822319447994232},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.47571760416030884},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4622836410999298},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4202965795993805},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2976216673851013}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9298428297042847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8093560338020325},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7159921526908875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7141287326812744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7032257318496704},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6505112648010254},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5700714588165283},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4822319447994232},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.47571760416030884},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4622836410999298},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4202965795993805},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2976216673851013},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2022.3233125","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3233125","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c13ce20f1930404098c2d590c1c86d32","is_oa":true,"landing_page_url":"https://doaj.org/article/c13ce20f1930404098c2d590c1c86d32","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 1085-1099 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2022.3233125","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3233125","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"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/G2659737875","display_name":null,"funder_award_id":"42271324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5116539810","display_name":null,"funder_award_id":"BK20221506","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W2001298023","https://openalex.org/W2162915993","https://openalex.org/W2187089797","https://openalex.org/W2261059368","https://openalex.org/W2314785379","https://openalex.org/W2464755555","https://openalex.org/W2603834682","https://openalex.org/W2743255627","https://openalex.org/W2764276316","https://openalex.org/W2800371750","https://openalex.org/W2808098982","https://openalex.org/W2894165434","https://openalex.org/W2898377853","https://openalex.org/W2914331134","https://openalex.org/W2940678725","https://openalex.org/W2942454403","https://openalex.org/W2952956606","https://openalex.org/W2961290969","https://openalex.org/W2991616716","https://openalex.org/W3003552243","https://openalex.org/W3011495011","https://openalex.org/W3031015423","https://openalex.org/W3046027728","https://openalex.org/W3047443805","https://openalex.org/W3075397214","https://openalex.org/W3103695279","https://openalex.org/W3105357426","https://openalex.org/W3105492824","https://openalex.org/W3122028341","https://openalex.org/W3122774149","https://openalex.org/W3133902755","https://openalex.org/W3138725786","https://openalex.org/W3140885850","https://openalex.org/W3168931281","https://openalex.org/W3171007011","https://openalex.org/W3181729304","https://openalex.org/W3182928821","https://openalex.org/W3195858154","https://openalex.org/W3202805802","https://openalex.org/W3205614732","https://openalex.org/W4206307542","https://openalex.org/W4210692941","https://openalex.org/W4225630686","https://openalex.org/W4239510810","https://openalex.org/W4281394103","https://openalex.org/W4281568429","https://openalex.org/W4285106080","https://openalex.org/W4288076010","https://openalex.org/W4295308382"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W4394638984","https://openalex.org/W3112261185","https://openalex.org/W2565656575","https://openalex.org/W3023590808"],"abstract_inverted_index":{"Deep":[0],"learning":[1,29,150,176,198],"(DL)":[2],"exhibits":[3],"commendable":[4],"performance":[5,24],"in":[6,44,64,202,212],"hyperspectral":[7,184],"image":[8],"(HSI)":[9],"classification":[10,200,205],"because":[11,81],"of":[12,25,60,82,204,214,225],"its":[13],"powerful":[14],"feature":[15,160],"expression":[16],"ability.":[17],"Siamese":[18],"neural":[19,46,53,96,109,134],"network":[20,54,84,97,110,135],"further":[21],"improves":[22],"the":[23,49,65,73,88,118,132,143,156,192],"DL":[26],"models":[27,77],"by":[28],"similarities":[30],"within-class":[31],"and":[32,113,126,159,166,177,195,207,228],"differences":[33],"between-class":[34],"from":[35],"sample":[36],"pairs.":[37],"However,":[38],"there":[39],"are":[40,171],"still":[41],"some":[42],"limitations":[43],"siamese":[45,52,95,108,133],"network.":[47],"On":[48,72],"one":[50],"hand,":[51,75],"usually":[55],"needs":[56],"a":[57,93,107,147,163,167],"large":[58],"number":[59],"negative":[61,138],"pair":[62,139],"samples":[63,237],"training":[66],"process,":[67],"leading":[68],"to":[69,116,130,154],"computing":[70],"overhead.":[71],"other":[74,193],"current":[76],"may":[78],"lack":[79],"interpretability":[80,158],"complex":[83],"structure.":[85],"To":[86],"overcome":[87],"above":[89],"limitations,":[90],"we":[91,105,122],"propose":[92],"spectral-spatial":[94,119],"with":[98,111,210],"bag-of-features":[99,148],"(S3BoF)":[100],"for":[101,174,221],"HSI":[102,199],"classification.":[103,178],"First,":[104],"use":[106],"3-D":[112],"2-D":[114],"convolutions":[115],"extract":[117],"features.":[120],"Second,":[121],"introduce":[123],"stop-gradient":[124],"operation":[125],"prediction":[127],"head":[128],"structure":[129],"make":[131],"work":[136],"without":[137],"samples,":[140],"thus":[141],"reducing":[142],"computational":[144],"burden.":[145],"Third,":[146],"(BoF)":[149],"module":[151],"is":[152],"introduced":[153],"enhance":[155],"model":[157],"representation.":[161],"Finally,":[162],"symmetric":[164],"loss":[165,170],"cross":[168],"entropy":[169],"respectively":[172],"used":[173],"contrastive":[175],"Experiments":[179],"results":[180],"on":[181],"four":[182],"common":[183],"datasets":[185],"indicated":[186],"that":[187],"S3BoF":[188],"performs":[189],"better":[190],"than":[191],"traditional":[194],"state-of-the-art":[196],"deep":[197],"methods":[201],"terms":[203,213],"accuracy":[206],"generalization":[208],"performance,":[209],"improvements":[211],"OA":[215],"around":[216],"1.40%\u201330.01%,":[217],"0.27%\u20138.65%,":[218],"0.37%\u20136.27%,":[219],"0.22%\u20136.64%":[220],"Indian":[222],"Pines,":[223],"University":[224],"Pavia,":[226],"Salinas,":[227],"Yellow":[229],"River":[230],"Delta":[231],"datasets,":[232],"respectively,":[233],"under":[234],"5%":[235],"labeled":[236],"per":[238],"class.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
