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AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification

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dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorxue, yu
dc.contributor.authorHaut, Juan M.
dc.contributor.authorPlaza, Antonio
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0002-9069-7547
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0002-9613-1659
dc.date.accessioned2024-11-18T12:19:54Z
dc.date.available2024-11-18T12:19:54Z
dc.date.issued2023
dc.descriptionThe registered version of this article, first published in “IEEE Transactions on Geoscience and Remote Sensing, 61, 2023", is available online at the publisher's website: IEEE, https://doi.org/10.1109/TGRS.2023.3272639 La versión registrada de este artículo, publicado por primera vez en “IEEE Transactions on Geoscience and Remote Sensing, 61, 2023", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/TGRS.2023.3272639
dc.description.abstractConvolutional models have provided outstanding performance in the analysis of hyperspectral images (HSIs). These architectures are carefully designed to extract intricate information from nonlinear features for classification tasks. Notwithstanding their results, model architectures are manually engineered and further optimized for generalized feature extraction. In general terms, deep architectures are time-consuming for complex scenarios, since they require fine-tuning. Neural architecture search (NAS) has emerged as a suitable approach to tackle this shortcoming. In parallel, modern attention-based methods have boosted the recognition of sophisticated features. The search for optimal neural architectures combined with attention procedures motivates the development of this work. This article develops a new method to automatically design and optimize convolutional neural networks (CNNs) for HSI classification using channel-based attention mechanisms. Specifically, 1-D and spectral–spatial (3-D) classifiers are considered to handle the large amount of information contained in HSIs from different perspectives. Furthermore, the proposed automatic attention-based CNN ( AAtt-CNN ) method meets the requirement to lower the large computational overheads associated with architectural search. It is compared with current state-of-the-art (SOTA) classifiers. Our experiments, conducted using a wide range of HSI images, demonstrate that AAtt-CNN succeeds in finding optimal architectures for classification, leading to SOTA results.en
dc.description.versionversión final
dc.identifier.citationMercedes E Paoletti, Sergio Moreno-Álvarez, Yu Xue, Juan M Haut, Antonio Plaza. "AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification". IEEE Transactions on Geoscience and Remote Sensing, 61, 03 May 2023, 1-18.
dc.identifier.doihttps://doi.org/10.1109/TGRS.2023.3272639
dc.identifier.issn0196-2892, eISSN: 1558-0644
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24405
dc.journal.titleIEEE Transactions on Geoscience and Remote Sensing
dc.journal.volume61
dc.language.isoen
dc.page.final18
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.titleAAtt-CNN: Automatic Attention-Based Convolutional Neural 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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