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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6264121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lvfei hyperspectralimageclassificationbasedonimprovedrotationforestalgorithm AT hanmin hyperspectralimageclassificationbasedonimprovedrotationforestalgorithm |