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Multiple Attention-Guided Capsule Networks for Hyperspectral Image Classification

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dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorHaut, Juan M.
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.date.accessioned2024-11-18T10:27:44Z
dc.date.available2024-11-18T10:27:44Z
dc.date.issued2022
dc.descriptionThe registered version of this article, first published in “IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022", is available online at the publisher's website: IEEE, https://doi.org/10.1109/TGRS.2021.3135506 La versión registrada de este artículo, publicado por primera vez en “IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/TGRS.2021.3135506
dc.description.abstractThe profound impact of deep learning and particularly of convolutional neural networks (CNNs) in automatic image processing has been decisive for the progress and evolution of remote sensing (RS) hyperspectral imaging (HSI) processing. Indeed, CNNs have stated themselves as the current state of the art, reaching unparalleled results in HSI classification. However, most CNNs were designed for RGB images, and their direct application to HSI data analysis could lead to nonoptimal solutions. Moreover, CNNs perform classification based on the identification of specific features, neglecting the spatial relationships between different features (i.e., their arrangement) due to pooling techniques. The capsule network (CapsNet) architecture is an attempt to overcome this drawback by nesting several neural layers within a capsule, connected by dynamic routing, both to identify not only the presence of a feature but also its instantiation parameters and to learn the relationships between different features. Although this mechanism improves the data representations, enhancing the classification of HSI data, it still acts as a black box, without control of the most relevant features for classification purposes. Indeed, important features could be discriminated against. In this article, a new multiple attention-guided CapsNet is proposed to improve feature processing for RS-HSIs’ classification, both to improve computational efficiency (in terms of parameters) and increase accuracy. Hence, the most representative visual parts of the images are identified using a detailed feature extractor coupled with attention mechanisms. Extensive experimental results have been obtained on five real datasets, demonstrating the great potential of the proposed method compared to other state-of-the-art classifiers.en
dc.description.versionversión publicada
dc.identifier.citationM. E. Paoletti, S. Moreno-Álvarez and J. M. Haut, "Multiple Attention-Guided Capsule Networks for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-20, 2022, Art no. 5520420, doi: 10.1109/TGRS.2021.3135506
dc.identifier.doihttps://doi.org/10.1109/TGRS.2021.3135506
dc.identifier.issn0196-2892, eISSN: 1558-0644
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24401
dc.journal.titleIEEE Transactions on Geoscience and Remote Sensing
dc.journal.volume60
dc.language.isoen
dc.page.final20
dc.page.initial1
dc.publisherIEEE
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsattentionen
dc.subject.keywordscapsule network (CapsNet)en
dc.subject.keywordsconvolutional neural network (CNN)en
dc.subject.keywordsfeatureen
dc.subject.keywordshyperspectral imaging (HSI)en
dc.titleMultiple Attention-Guided Capsule Networks for Hyperspectral Image Classificationen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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