Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation
Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description feat...
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ftdoajarticles:oai:doaj.org/article:97b4a660137348eb8e766db27061345f 2023-05-15T18:02:44+02:00 Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation Yanfei Zhong Rui Huang Ji Zhao Bei Zhao Tingting Liu 2018-02-01T00:00:00Z https://doi.org/10.3390/rs10020233 https://doaj.org/article/97b4a660137348eb8e766db27061345f EN eng MDPI AG http://www.mdpi.com/2072-4292/10/2/233 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10020233 https://doaj.org/article/97b4a660137348eb8e766db27061345f Remote Sensing, Vol 10, Iss 2, p 233 (2018) aurora image classification multiple features 1-D histogram latent Dirichlet allocation (LDA) probabilistic topic model (PTM) Science Q article 2018 ftdoajarticles https://doi.org/10.3390/rs10020233 2022-12-31T03:55:11Z Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description features is usually so high that it reduces the efficiency. In this paper, through combining multiple features extracted from aurora images, an aurora image classification method based on multi-feature latent Dirichlet allocation (AI-MFLDA) is proposed. Different types of features, whether local or global, discrete or continuous, can be integrated after being transformed to one-dimensional (1-D) histograms, and the dimension of the description features can be reduced due to using only a few topics to represent the aurora images. In the experiments, according to the classification system provided by the Polar Research Institute of China, a four-class aurora image dataset was tested and three types of features (MeanStd, scale-invariant feature transform (SIFT), and shape-based invariant texture index (SITI)) were utilized. The experimental results showed that, compared to the traditional methods, the proposed AI-MFLDA is able to achieve a better performance with 98.2% average classification accuracy while maintaining a low feature dimension. Article in Journal/Newspaper Polar Research Institute of China Directory of Open Access Journals: DOAJ Articles Polar Science 28 100659 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
aurora image classification multiple features 1-D histogram latent Dirichlet allocation (LDA) probabilistic topic model (PTM) Science Q |
spellingShingle |
aurora image classification multiple features 1-D histogram latent Dirichlet allocation (LDA) probabilistic topic model (PTM) Science Q Yanfei Zhong Rui Huang Ji Zhao Bei Zhao Tingting Liu Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation |
topic_facet |
aurora image classification multiple features 1-D histogram latent Dirichlet allocation (LDA) probabilistic topic model (PTM) Science Q |
description |
Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description features is usually so high that it reduces the efficiency. In this paper, through combining multiple features extracted from aurora images, an aurora image classification method based on multi-feature latent Dirichlet allocation (AI-MFLDA) is proposed. Different types of features, whether local or global, discrete or continuous, can be integrated after being transformed to one-dimensional (1-D) histograms, and the dimension of the description features can be reduced due to using only a few topics to represent the aurora images. In the experiments, according to the classification system provided by the Polar Research Institute of China, a four-class aurora image dataset was tested and three types of features (MeanStd, scale-invariant feature transform (SIFT), and shape-based invariant texture index (SITI)) were utilized. The experimental results showed that, compared to the traditional methods, the proposed AI-MFLDA is able to achieve a better performance with 98.2% average classification accuracy while maintaining a low feature dimension. |
format |
Article in Journal/Newspaper |
author |
Yanfei Zhong Rui Huang Ji Zhao Bei Zhao Tingting Liu |
author_facet |
Yanfei Zhong Rui Huang Ji Zhao Bei Zhao Tingting Liu |
author_sort |
Yanfei Zhong |
title |
Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation |
title_short |
Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation |
title_full |
Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation |
title_fullStr |
Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation |
title_full_unstemmed |
Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation |
title_sort |
aurora image classification based on multi-feature latent dirichlet allocation |
publisher |
MDPI AG |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10020233 https://doaj.org/article/97b4a660137348eb8e766db27061345f |
genre |
Polar Research Institute of China |
genre_facet |
Polar Research Institute of China |
op_source |
Remote Sensing, Vol 10, Iss 2, p 233 (2018) |
op_relation |
http://www.mdpi.com/2072-4292/10/2/233 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10020233 https://doaj.org/article/97b4a660137348eb8e766db27061345f |
op_doi |
https://doi.org/10.3390/rs10020233 |
container_title |
Polar Science |
container_volume |
28 |
container_start_page |
100659 |
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1766173352763850752 |