Tensor Ensemble of Ground-Based Cloud Sequences: Its Modeling, Classification, and Synthesis

Since clouds are one of the most important meteorological phenomena related to the hydrological cycle and affect Earth radiation balance and climate changes, cloud analysis is a crucial issue in meteorological research. Most researchers only consider the classification task of cloud images while les...

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Veröffentlicht in:IEEE geoscience and remote sensing letters 2013-09, Vol.10 (5), p.1190-1194
Hauptverfasser: Liu, Shuang, Wang, Chunheng, Xiao, Baihua, Zhang, Zhong, Cao, Xiaozhong
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creator Liu, Shuang
Wang, Chunheng
Xiao, Baihua
Zhang, Zhong
Cao, Xiaozhong
description Since clouds are one of the most important meteorological phenomena related to the hydrological cycle and affect Earth radiation balance and climate changes, cloud analysis is a crucial issue in meteorological research. Most researchers only consider the classification task of cloud images while less attention has been paid to the synthesis one. In addition, all the existing research on cloud identification from sky images is based on single cloud images. However, the cloud-measuring devices on the ground actually take one image of the clouds every few minutes and collect a series of cloud images. Thus, the existing methods neglect the temporal information exhibited by contiguous cloud images. To overcome this drawback, in this letter we treat ground-based cloud sequences (GCSs) as dynamic texture. We then propose the Tensor Ensemble of Ground-based Cloud Sequences (eTGCS) model which represents the ensemble of GCSs in a tensor manner. In the eTGCS model, all GCSs form a single tensor, and each GCS is a subtensor of the single tensor. There are two main characteristics of the eTGCS model: 1) All GCSs share an identical mode subspace, which makes the classification convenient, and 2) a new GCS can be synthesized as long as the parameters of the eTGCS model are used. Therefore, less storage space is required. Comprehensive experiments are conducted to prove the superiority of our eTGCS model. The classification accuracy achieves 92.31%, and the synthesized GCSs are similar to the original ones in visual appearance.
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There are two main characteristics of the eTGCS model: 1) All GCSs share an identical mode subspace, which makes the classification convenient, and 2) a new GCS can be synthesized as long as the parameters of the eTGCS model are used. Therefore, less storage space is required. Comprehensive experiments are conducted to prove the superiority of our eTGCS model. 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subjects Accuracy
Autoregressive processes
Classification
Clouds
Feature extraction
Ground-based cloud sequences (GCSs)
Mathematical analysis
Mathematical model
Meteorology
Studies
Sun
Surface layer
Synthesis
Tensile stress
tensor ensemble
Tensors
Texture
title Tensor Ensemble of Ground-Based Cloud Sequences: Its Modeling, Classification, and Synthesis
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