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Parameter-Free Attention Network for Spectral–Spatial Hyperspectral Image Classification

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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.authorKumar Roy, Swalpa
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/0000-0002-6580-3977
dc.date.accessioned2024-11-19T08:05:39Z
dc.date.available2024-11-19T08:05:39Z
dc.date.issued2023
dc.descriptionThe registered version of this article, first published in “IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023", is available online at the publisher's website: IEEE, https://doi.org/10.1109/TGRS.2023.3295097 La versión registrada de este artículo, publicado por primera vez en “IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/TGRS.2023.3295097
dc.description.abstractHyperspectral images (HSIs) comprise plenty of information in the spatial and spectral domain, which is highly beneficial for performing classification tasks in a very accurate way. Recently, attention mechanisms have been widely used in the HSI classification due to their ability to extract relevant spatial and spectral features. Notwithstanding their positive results, most of the attentional strategies usually introduce a significant number of parameters to be trained, making the models more complex and increasing the computational load. In this article, we develop a new parameter-free attention network for HSI classification. The main advantage of our model is that it does not add parameters to the original network (as opposed to other state-of-the-art approaches) while providing higher classification accuracies. Extensive experimental validations and quantitative comparisons are conducted—using different benchmark HSIs—to illustrate these advantages. The code is available on https://github.com/mhaut/Free2Resneten
dc.description.versionversión publicada
dc.identifier.citationM. E. Paoletti et al., "Parameter-Free Attention Network for Spectral–Spatial Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023, Art no. 5516817, doi: 10.1109/TGRS.2023.3295097
dc.identifier.doihttps://doi.org/10.1109/TGRS.2023.3295097
dc.identifier.issn0196-2892, eISSN: 1558-0644
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24419
dc.journal.titleIEEE Transactions on Geoscience and Remote Sensing
dc.journal.volume61
dc.language.isoen
dc.page.initial5516817
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.keywordsfeature extractionen
dc.subject.keywordscomputational modelingen
dc.subject.keywordstrainingen
dc.subject.keywordsthree-dimensional displaysen
dc.subject.keywordshyperspectral imagingen
dc.subject.keywordsconvolutionen
dc.subject.keywordstask analysisen
dc.titleParameter-Free Attention Network for Spectral–Spatial 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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