{"id":"https://openalex.org/W4312097002","doi":"https://doi.org/10.1109/cisp-bmei56279.2022.9980165","title":"PCANet Combined with Residual Connection for Hyperspectral Image Classification","display_name":"PCANet Combined with Residual Connection for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-11-05","ids":{"openalex":"https://openalex.org/W4312097002","doi":"https://doi.org/10.1109/cisp-bmei56279.2022.9980165"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei56279.2022.9980165","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei56279.2022.9980165","pdf_url":null,"source":{"id":"https://openalex.org/S4363605502","display_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5023770516","display_name":"Mianpeng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mianpeng Li","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology,Guangzhou,China,510006","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050455072","display_name":"Xiang Xu","orcid":"https://orcid.org/0000-0002-4388-1915"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Xu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Zhongshan Institution,Artificial Intelligence and Computer Vision Laboratory,Zhongshan,China,528402"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Zhongshan Institution,Artificial Intelligence and Computer Vision Laboratory,Zhongshan,China,528402","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023770516"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22491834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9958000183105469,"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":0.9843000173568726,"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/residual","display_name":"Residual","score":0.7218921780586243},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7036678194999695},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6185687184333801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5625112056732178},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47129809856414795},{"id":"https://openalex.org/keywords/connection","display_name":"Connection (principal bundle)","score":0.4609539806842804},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41077107191085815},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3709612488746643},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3588714003562927},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14564654231071472},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09326183795928955}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7218921780586243},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7036678194999695},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6185687184333801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5625112056732178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47129809856414795},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.4609539806842804},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41077107191085815},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3709612488746643},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3588714003562927},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14564654231071472},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09326183795928955},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei56279.2022.9980165","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei56279.2022.9980165","pdf_url":null,"source":{"id":"https://openalex.org/S4363605502","display_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1616262590","https://openalex.org/W1865351371","https://openalex.org/W2029316659","https://openalex.org/W2041227601","https://openalex.org/W2072072671","https://openalex.org/W2075047788","https://openalex.org/W2090424610","https://openalex.org/W2106277226","https://openalex.org/W2143354507","https://openalex.org/W2157321686","https://openalex.org/W2159070926","https://openalex.org/W2166923144","https://openalex.org/W2231495490","https://openalex.org/W2346557146","https://openalex.org/W2500751094","https://openalex.org/W2520632654","https://openalex.org/W2527650001","https://openalex.org/W2542500166","https://openalex.org/W2587790406","https://openalex.org/W2737996023","https://openalex.org/W2757208835","https://openalex.org/W2764276316","https://openalex.org/W2770315464","https://openalex.org/W2782281103","https://openalex.org/W2811355488","https://openalex.org/W2891059222","https://openalex.org/W2894165434","https://openalex.org/W2900322278","https://openalex.org/W2914331134","https://openalex.org/W2942454403","https://openalex.org/W2954497554","https://openalex.org/W2983376237","https://openalex.org/W3046027728","https://openalex.org/W3102431071","https://openalex.org/W3102503590","https://openalex.org/W3122774149","https://openalex.org/W3206768769","https://openalex.org/W4240485910"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposed":[2,33,107],"a":[3],"relatively":[4],"lightweight":[5],"DL":[6,34],"model":[7,35],"for":[8,90],"the":[9,18,29,32,44,56,66,69,103,106,113,116],"classification":[10],"of":[11,49,55,65,105,115],"hyperspectral":[12,100],"imagery,":[13],"which":[14,110],"is":[15],"built":[16],"on":[17,43,98],"PCANet":[19],"backbone":[20],"framework":[21],"and":[22,52,79],"combined":[23,72],"with":[24,73],"residual":[25,74,117],"connection.":[26],"Compared":[27],"to":[28],"conventional":[30],"CNNs,":[31],"(Hereafter":[36],"dubbed":[37],"\u2018ResPCANet\u2019)":[38],"has":[39],"some":[40],"surprising":[41],"merits,":[42],"one":[45],"hand,":[46,71],"more":[47,77],"simpler":[48],"network":[50],"architecture":[51],"less":[53],"requirements":[54],"labelled":[57],"samples,":[58],"as":[59,61],"well":[60],"faster":[62],"learning":[63],"speed":[64],"network.":[67],"On":[68],"other":[70],"skip":[75,118],"connection,":[76],"diverse":[78],"complementary":[80],"features":[81],"that":[82],"derived":[83],"from":[84],"different":[85],"layers":[86],"can":[87],"be":[88],"fused":[89],"subsequent":[91],"classifier":[92],"construction.":[93],"Our":[94],"experiments,":[95],"carried":[96],"out":[97],"real":[99],"dataset,":[101],"revealed":[102],"effectiveness":[104],"ResPCANet":[108],"model,":[109],"further":[111],"demonstrated":[112],"superiority":[114],"connection":[119],"strategy.":[120]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}