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http://hdl.handle.net/10662/20396
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Campo DC | Valor | idioma |
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dc.contributor.author | Paoletti Ávila, Mercedes Eugenia | - |
dc.contributor.author | Mogollón Gutiérrez, Óscar | - |
dc.contributor.author | Moreno Álvarez, Sergio | - |
dc.contributor.author | Sancho Núñez, José Carlos | - |
dc.contributor.author | Haut Hurtado, Juan Mario | - |
dc.date.accessioned | 2024-02-08T09:07:36Z | - |
dc.date.available | 2024-02-08T09:07:36Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 19391404 | - |
dc.identifier.uri | http://hdl.handle.net/10662/20396 | - |
dc.description.abstract | Land-cover classification is an important topic for remotely sensed hyperspectral (HS) data exploitation. In this regard, HS classifiers have to face important challenges, such as the high spectral redundancy, as well as noise, present in the data, and the fact that obtaining accurate labeled training data for supervised classification is expensive and time-consuming. As a result, the availability of large amounts of training samples, needed to alleviate the so-called Hughes phenomenon, is often unfeasible in practice. The class-imbalance problem, which results from the uneven distribution of labeled samples per class, is also a very challenging factor for HS classifiers. In this article, a comprehensive review of oversampling techniques is provided, which mitigate the aforementioned issues by generating new samples for the minority classes. More specifically, this article pursues a twofold objective. First, it reviews the most relevant oversampling methods that can be adopted according to the nature of HS data. Second, it provides a comprehensive experimental study and comparison, which are useful to derive practical conclusions about the performance of oversampling techniques in different HS image-based applications. | es_ES |
dc.format.extent | 18 p. | - |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hiperespectral (HS) | es_ES |
dc.subject | Hyperspectral (HS) | es_ES |
dc.subject | desbalanceo | es_ES |
dc.subject | imbalance | es_ES |
dc.subject | aprendizaje automático | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | sobremuestreo | es_ES |
dc.subject | oversampling | es_ES |
dc.title | A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification | es_ES |
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 | 1203 Ciencia de Los Ordenadores | - |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | M. E. Paoletti, O. Mogollon-Gutierrez, S. Moreno-Álvarez, J. C. Sancho and J. M. Haut, "A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 5297-5314, 2023, doi: 10.1109/JSTARS.2023.3279506. | es_ES |
dc.type.version | publishedVersion | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Ingeniería de Sistemas Informáticos y Telemáticos | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento Tecnología computadores y de las comunicaciones. | - |
dc.relation.publisherversion | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10132491 | es_ES |
dc.identifier.doi | 10.1109/JSTARS.2023.3279506 | - |
dc.identifier.publicationtitle | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | es_ES |
dc.identifier.publicationfirstpage | 5297 | es_ES |
dc.identifier.publicationlastpage | 5314 | es_ES |
dc.identifier.publicationvolume | 16 | es_ES |
dc.identifier.orcid | 0000-0003-1030-3729 | es_ES |
dc.identifier.orcid | 0000-0003-2980-9236 | es_ES |
dc.identifier.orcid | 0000-0002-1858-9920 | es_ES |
dc.identifier.orcid | 0000-0002-4584-6945 | es_ES |
dc.identifier.orcid | 0000-0001-6701-961X | es_ES |
Colección: | DISIT - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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JSTARS_2023_3279506.pdf | 6,31 MB | Adobe PDF | Descargar |
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