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dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorHaut, Juan M.
dc.contributor.authorFernandez-Beltran, Ruben
dc.contributor.authorPlaza, Javier
dc.contributor.authorPlaza, Antonio
dc.contributor.authorLi, Jun
dc.contributor.authorPla, Filiberto
dc.date.accessioned2018-12-11T10:53:05Z
dc.date.available2018-12-11T10:53:05Z
dc.date.issued2018-10
dc.identifier.citationPAOLETTI, Mercedes E., et al. Capsule Networks for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2018.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/178002
dc.description.abstractConvolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral-spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral-spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton's capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral-spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral-spatial capsules, which are able to learn HSI spectral-spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral-spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.ca_CA
dc.format.extent16 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherIEEEca_CA
dc.rights© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectCapsule networks (CapsNets)ca_CA
dc.subjectconvolutional neural networks (CNNs)ca_CA
dc.subjecthyperspectral imaging (HSI)ca_CA
dc.titleCapsule Networks for Hyperspectral Image Classificationca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1109/TGRS.2018.2871782
dc.relation.projectIDJunta de Extremadura (GR15005) ; Generalitat Valenciana (APOSTD/2017/007) ; Spanish Ministry of Economy (projects ESP2016-79503-C2-2-P and TIN2015-63646-C5-5-R) ; National Natural Science Foundation of China (Grant 61771496) ; National Key Research and Development Program of China (Grant 2017YFB0502900) ; Guangdong Provincial Natural Science Foundation (Grant 2016A030313254)ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://ieeexplore.ieee.org/document/8509610ca_CA
dc.contributor.funderMinisterio de Educación, Spain (Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016)ca_CA
dc.type.versioninfo:eu-repo/semantics/submittedVersionca_CA


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