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Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on...

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Detalles Bibliográficos
Autores principales: Lv, Fei, Han, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264121/
https://www.ncbi.nlm.nih.gov/pubmed/30360556
http://dx.doi.org/10.3390/s18113601
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author Lv, Fei
Han, Min
author_facet Lv, Fei
Han, Min
author_sort Lv, Fei
collection PubMed
description Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.
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spelling pubmed-62641212018-12-12 Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm Lv, Fei Han, Min Sensors (Basel) Article Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method. MDPI 2018-10-23 /pmc/articles/PMC6264121/ /pubmed/30360556 http://dx.doi.org/10.3390/s18113601 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lv, Fei
Han, Min
Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
title Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
title_full Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
title_fullStr Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
title_full_unstemmed Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
title_short Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
title_sort hyperspectral image classification based on improved rotation forest algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264121/
https://www.ncbi.nlm.nih.gov/pubmed/30360556
http://dx.doi.org/10.3390/s18113601
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