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Deep Attention-Driven HSI Scene Classification Based on Inverted Dot-Product

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dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorTao, Xuanwen
dc.contributor.authorhan, lirong
dc.contributor.authorWu, Zhaoyue
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorHaut, Juan M.
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0003-1093-0079
dc.contributor.orcidhttps://orcid.org/0000-0002-8613-7037
dc.contributor.orcidhttps://orcid.org/0000-0002-6797-2440
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.coverage.spatialKuala Lumpur, Malaysia
dc.coverage.temporal2022-07-22
dc.date.accessioned2024-11-19T10:18:19Z
dc.date.available2024-11-19T10:18:19Z
dc.date.issued2022
dc.descriptionThe registered version of this article, first published in “Institute of Electrical and Electronics Engineers Inc, 2022", is available online at the publisher's website: IEEE, https://doi.org/10.1109/IGARSS46834.2022.9883028 La versión registrada de este artículo, publicado por primera vez en “Institute of Electrical and Electronics Engineers Inc, 2022", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/IGARSS46834.2022.9883028
dc.description.abstractCapsule networks have been a breakthrough in the field of automatic image analysis, opening a new frontier in the art for image classification. Nevertheless, these models were initially designed for RGB images and naively applying these techniques to remote sensing hyperspectral images (HSI) may lead to sub-optimal behaviour, blowing up the number of parameters needed to train the model or not correctly modeling the spectral relations between the different layers of the scene. To overcome this drawback, this work implements a new capsule-based architecture with attention mechanism to improve the HSI data processing. The attention mechanism is applied during the concurrent iterative routing procedure through an inverted dot-product attentionen
dc.description.versionversión publicada
dc.identifier.citationM. E. Paoletti, X. Tao, L. Han, Z. Wu, S. Moreno-Álvarez and J. M. Haut, "Deep Attention-Driven HSI Scene Classification Based on Inverted Dot-Product," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 1380-1383, doi: 10.1109/IGARSS46834.2022.9883028
dc.identifier.doihttps://doi.org/10.1109/IGARSS46834.2022.9883028
dc.identifier.isbn978-1-6654-2792-0
dc.identifier.issn2153-7003| eISSN: 2153-6996
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24424
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.congressIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022
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.keywordsArten
dc.subject.keywordsComputational modelingen
dc.subject.keywordsComputer architectureen
dc.subject.keywordsRoutingen
dc.subject.keywordsData processingen
dc.subject.keywordsIterative methodsen
dc.subject.keywordsImage classificationen
dc.titleDeep Attention-Driven HSI Scene Classification Based on Inverted Dot-Producten
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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