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http://hdl.handle.net/10662/20257
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Campo DC | Valor | idioma |
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dc.contributor.author | Haut Hurtado, Juan Mario | - |
dc.contributor.author | Paoletti Ávila, Mercedes Eugenia | - |
dc.contributor.author | Plaza Miguel, Javier | - |
dc.contributor.author | Li, Jun | - |
dc.contributor.author | Plaza, Antonio | - |
dc.date.accessioned | 2024-02-07T11:19:09Z | - |
dc.date.available | 2024-02-07T11:19:09Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0196-2892 | - |
dc.identifier.uri | http://hdl.handle.net/10662/20257 | - |
dc.description.abstract | Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification. | - |
dc.description.sponsorship | This paper was supported by Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016. This work has also been supported by Junta de Extremadura (decreto 297/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura, Ref. GR15005). | - |
dc.format.extent | 21 p. | es_ES |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | - |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | Redes neuronales convolucionales bayesianas | es_ES |
dc.subject | Aprendizaje activo | es_ES |
dc.subject | Clasificación de imágenes de teledetección hiperespectral | es_ES |
dc.subject | Bayesian convolutional neural network | en_Us |
dc.subject | Active learning | en_Us |
dc.subject | Hyperspectral remote sensing image classification | en_Us |
dc.title | Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach | en_US |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | es_ES |
dc.subject.unesco | 3304 Tecnología de Los Ordenadores | - |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | J. M. Haut, M. E. Paoletti, J. Plaza, J. Li and A. Plaza, "Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 11, pp. 6440-6461, Nov. 2018, doi: 10.1109/TGRS.2018.2838665 | - |
dc.type.version | publishedVersion | - |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Tecnología de los Computadores y de las Comunicaciones | es_ES |
dc.contributor.affiliation | Sun Yat-sen University. China | en_Us |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8390931 | - |
dc.identifier.publicationtitle | IEEE Transactions on Geoscience and Remote Sensing | es_ES |
dc.identifier.publicationissue | 11 | - |
dc.identifier.publicationfirstpage | 6440 | es_ES |
dc.identifier.publicationlastpage | 6461 | es_ES |
dc.identifier.publicationvolume | 56 | es_ES |
dc.identifier.orcid | 0000-0001-6701-961X | es_ES |
dc.identifier.orcid | 0000-0003-1030-3729 | - |
dc.identifier.orcid | 0000-0002-2384-9141 | - |
dc.identifier.orcid | 0000-0002-9613-1659 | - |
Colección: | DTCYC - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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TGRS_2018_2838665.pdf | 2,99 MB | Adobe PDF | Descargar |
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