Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10662/20314
Registro completo de Metadatos
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Paoletti Ávila, Mercedes Eugenia | - |
dc.contributor.author | Haut Hurtado, Juan Mario | - |
dc.contributor.author | Plaza Miguel, Javier | - |
dc.contributor.author | Plaza, Antonio | - |
dc.date.accessioned | 2024-02-07T12:27:00Z | - |
dc.date.available | 2024-02-07T12:27:00Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0924-2716 | - |
dc.identifier.uri | http://hdl.handle.net/10662/20314 | - |
dc.description.abstract | Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention in this field. CNNs have proved to be very effective in areas such as image recognition and classification, especially for the classification of large sets composed by two-dimensional images. However, their application to multispectral and hyperspectral images faces some challenges, especially related to the processing of the high-dimensional information contained in multidimensional data cubes. This results in a significant increase in computation time. In this paper, we present a new CNN architecture for the classification of hyperspectral images. The proposed CNN is a 3-D network that uses both spectral and spatial information. It also implements a border mirroring strategy to effectively process border areas in the image, and has been efficiently implemented using graphics processing units (GPUs). Our experimental results indicate that the proposed network performs accurately and efficiently, achieving a reduction of the computation time and increasing the accuracy in the classification of hyperspectral images when compared to other traditional ANN techniques. | en_Us |
dc.description.sponsorship | This work has been 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 | 57 p. | es_ES |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | - |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | Imagen hiperespectral | es_ES |
dc.subject | Aprendizaje profundo | es_ES |
dc.subject | Redes neuronales convolucionales | es_ES |
dc.subject | Clasificación | es_ES |
dc.subject | Unidades de procesamiento de gráficos | es_ES |
dc.subject | Hyperspectral imaging | en_Us |
dc.subject | Deep learning | en_Us |
dc.subject | Convolutional neural networks | en_Us |
dc.subject | Classification | en_Us |
dc.subject | Graphics processing units | en_Us |
dc.title | A new deep convolutional neural network for fast hyperspectral image classification | es_ES |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | - |
dc.subject.unesco | 3304 Tecnología de Los Ordenadores | - |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A. (2018). A new deep convolutional neural network for fast hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 145(A), 120-147. DOI: https://doi.org/10.1016/j.isprsjprs.2017.11.021 | - |
dc.type.version | Draft | - |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Tecnología de los Computadores y de las Comunicaciones | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0924271617303660 | - |
dc.identifier.doi | 10.1016/j.isprsjprs.2017.11.021 | - |
dc.identifier.publicationtitle | ISPRS Journal of Photogrammetry and Remote Sensing | - |
dc.identifier.publicationissue | Part A | - |
dc.identifier.publicationfirstpage | 120 | es_ES |
dc.identifier.publicationlastpage | 147 | es_ES |
dc.identifier.publicationvolume | 145 | es_ES |
dc.identifier.e-issn | 1872-8235 | - |
dc.identifier.orcid | 0000-0003-1030-3729 | es_ES |
dc.identifier.orcid | 0000-0001-6701-961X | - |
dc.identifier.orcid | 0000-0002-2384-9141 | - |
dc.identifier.orcid | 0000-0002-9613-1659 | - |
Colección: | DTCYC - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
j_isprsjprs_2017_11_021_preprint.pdf | 6,78 MB | Adobe PDF | Descargar |
Este elemento está sujeto a una licencia Licencia Creative Commons