{"id":"https://openalex.org/W3183280703","doi":"https://doi.org/10.1109/whispers52202.2021.9484019","title":"A Joint Spatial-Spectral Representation Based Capsule Network for Hyperspectral Image Classification","display_name":"A Joint Spatial-Spectral Representation Based Capsule Network for Hyperspectral Image Classification","publication_year":2021,"publication_date":"2021-03-24","ids":{"openalex":"https://openalex.org/W3183280703","doi":"https://doi.org/10.1109/whispers52202.2021.9484019","mag":"3183280703"},"language":"en","primary_location":{"id":"doi:10.1109/whispers52202.2021.9484019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers52202.2021.9484019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Qun Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I3019309368","display_name":"North Minzu University","ror":"https://ror.org/05xjevr11","country_code":"CN","type":"education","lineage":["https://openalex.org/I3019309368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qun Zhou","raw_affiliation_strings":["School of Computer Science and Engineering, North Minzu University, Yinchuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China","institution_ids":["https://openalex.org/I3019309368"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046538842","display_name":"Wenxing Bao","orcid":"https://orcid.org/0000-0002-0267-5824"},"institutions":[{"id":"https://openalex.org/I3019309368","display_name":"North Minzu University","ror":"https://ror.org/05xjevr11","country_code":"CN","type":"education","lineage":["https://openalex.org/I3019309368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxing Bao","raw_affiliation_strings":["School of Computer Science and Engineering, North Minzu University, Yinchuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China","institution_ids":["https://openalex.org/I3019309368"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009140098","display_name":"Xiaowu Zhang","orcid":"https://orcid.org/0000-0002-4840-5520"},"institutions":[{"id":"https://openalex.org/I3019309368","display_name":"North Minzu University","ror":"https://ror.org/05xjevr11","country_code":"CN","type":"education","lineage":["https://openalex.org/I3019309368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaowu Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, North Minzu University, Yinchuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China","institution_ids":["https://openalex.org/I3019309368"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052533985","display_name":"Xuan Ma","orcid":"https://orcid.org/0000-0002-8837-0908"},"institutions":[{"id":"https://openalex.org/I3019309368","display_name":"North Minzu University","ror":"https://ror.org/05xjevr11","country_code":"CN","type":"education","lineage":["https://openalex.org/I3019309368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Ma","raw_affiliation_strings":["School of Computer Science and Engineering, North Minzu University, Yinchuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, North Minzu University, Yinchuan, China","institution_ids":["https://openalex.org/I3019309368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3019309368"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9865000247955322,"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":0.9732999801635742,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8818493485450745},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7275046110153198},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7264043688774109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7092477083206177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6195175647735596},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4979360103607178},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.49056190252304077},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4649945795536041},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.44656848907470703},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4411733150482178},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4248409569263458},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.420524537563324},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42019107937812805},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.19175994396209717},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1513468623161316},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06913915276527405},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.059155941009521484}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8818493485450745},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7275046110153198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7264043688774109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7092477083206177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6195175647735596},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4979360103607178},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.49056190252304077},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4649945795536041},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.44656848907470703},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4411733150482178},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4248409569263458},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.420524537563324},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42019107937812805},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.19175994396209717},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1513468623161316},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06913915276527405},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.059155941009521484},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers52202.2021.9484019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers52202.2021.9484019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W854541894","https://openalex.org/W1966041041","https://openalex.org/W2548476632","https://openalex.org/W2808098982","https://openalex.org/W2809287713","https://openalex.org/W2898381489","https://openalex.org/W2912371366","https://openalex.org/W2942454403","https://openalex.org/W2955541799","https://openalex.org/W2963495494","https://openalex.org/W2963703618","https://openalex.org/W2976820585","https://openalex.org/W2983062821","https://openalex.org/W2994308318","https://openalex.org/W3001222093","https://openalex.org/W3031696893","https://openalex.org/W3099004229","https://openalex.org/W3100011500","https://openalex.org/W3105357426","https://openalex.org/W6623517193","https://openalex.org/W6743446608","https://openalex.org/W6779450612"],"related_works":["https://openalex.org/W4200272877","https://openalex.org/W3034655717","https://openalex.org/W2337415362","https://openalex.org/W2546645752","https://openalex.org/W4312857205","https://openalex.org/W121273120","https://openalex.org/W2740820121","https://openalex.org/W2781623059","https://openalex.org/W3173596272","https://openalex.org/W2983327997"],"abstract_inverted_index":{"Recently,":[0],"capsule":[1,24,40,122],"networks":[2,25],"have":[3],"shown":[4],"excellent":[5],"performance":[6,22],"in":[7],"hyperspectral":[8],"image":[9],"(HSI)":[10],"classification.":[11],"However,":[12],"when":[13],"the":[14,20,60,83,90,97,112],"HSI":[15],"contains":[16],"complex":[17],"topographic":[18],"features,":[19],"classification":[21,50],"of":[23,62,80,92],"will":[26],"deteriorate.":[27],"To":[28],"address":[29],"this":[30],"issue,":[31],"we":[32,70],"propose":[33],"a":[34,72],"novel":[35],"joint":[36],"spatial-spectral":[37,46,67],"representation":[38],"based":[39],"network":[41],"(JSSR-CapsNet),":[42],"which":[43],"deeply":[44],"exploits":[45],"information":[47],"to":[48,58,77,95],"improve":[49],"performance.":[51],"Specifically,":[52],"JSSR-CapsNet":[53,110],"utilizes":[54],"an":[55],"attention":[56],"module":[57],"highlight":[59],"validity":[61],"sensitive":[63],"pixels":[64,81,94],"from":[65],"redundant":[66],"features.":[68],"Then,":[69],"built":[71],"dilated":[73],"convolutional":[74],"pyramid":[75],"(DCP)":[76],"take":[78],"aggregation":[79],"with":[82],"same":[84],"class":[85],"into":[86],"account,":[87],"and":[88,117],"suppress":[89],"interference":[91],"noisy":[93],"enhance":[96],"intra-class":[98],"consistency.":[99],"The":[100],"quantitative":[101],"results":[102],"conducted":[103],"on":[104],"two":[105],"real":[106],"HSIs":[107],"show":[108],"that":[109],"improves":[111],"overall":[113],"accuracy":[114],"by":[115],"1.86%":[116],"2.07%":[118],"respectively":[119],"over":[120],"state-of-the-art":[121],"networks.":[123]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2025-10-10T00:00:00"}