Cloud-type Classification of Ground-Based Images using Deep Learning

Abstract. Cloud types are important indicators of cloud characteristics and short-term weather forecasting. The meteorological researchers can benefit from the automatic cloud type recognition of massive images captured by the ground-based imagers. However, by far it is still of huge challenge to design a powerful discriminative classifier for cloud categorization. To tackle this difficulty, in this paper, we present an improved method with region covariance descriptors (RCovDs) and Riemannian Bag-of-Feature (BoF). RCovDs model the correlations among different dimensional features, that allows for a more discriminative representation. BoF is extended from Euclidean space to Riemannian manifold by k-Means clustering, in which Stein divergence is adopted as a similarity metric. The histogram feature is extracted by encoding RCovDs of the cloud image blocks with BoF-based codebook. The multi-class support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets validate the proposed method and exhibit the competitive performance against state-of-the-art methods.

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Improving cloud type classification of ground-based images using region covariance descriptors

Atmospheric Measurement Techniques ◽

10.5194/amt-14-737-2021 ◽

2021 ◽

Vol 14 (1) ◽

pp. 737-747

Author(s):

Yuzhu Tang ◽

Pinglv Yang ◽

Zeming Zhou ◽

Delu Pan ◽

Jianyu Chen ◽

...

Keyword(s):

Support Vector ◽

Cloud Type ◽

Training Samples ◽

Multiclass Support Vector Machine ◽

High Prediction ◽

Classification Of Images ◽

Region Covariance ◽

The Relationship ◽

Type Classification

Abstract. The distribution and frequency of occurrence of different cloud types affect the energy balance of the Earth. Automatic cloud type classification of images continuously observed by the ground-based imagers could help climate researchers find the relationship between cloud type variations and climate change. However, by far it is still a huge challenge to design a powerful discriminative classifier for cloud categorization. To tackle this difficulty, in this paper, we present an improved method with region covariance descriptors (RCovDs) and the Riemannian bag-of-feature (BoF) method. RCovDs model the correlations among different dimensional features, which allows for a more discriminative representation. BoF is extended from Euclidean space to Riemannian manifold by k-means clustering, in which Stein divergence is adopted as a similarity metric. The histogram feature is extracted by encoding RCovDs of the cloud image blocks with a BoF-based codebook. The multiclass support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets show that a very high prediction accuracy (more than 98 % on two datasets) can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods.

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Classifiers for Ground-Based Cloud Images Using Texture Features

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.931-932.1392 ◽

2014 ◽

Vol 931-932 ◽

pp. 1392-1396 ◽

Cited By ~ 2

Author(s):

Thitinan Kliangsuwan ◽

Apichat Heednacram

Keyword(s):

Nearest Neighbor ◽

Texture Features ◽

Correct Classification ◽

Ann Model ◽

Cloud Type ◽

Artificial Neural Network Ann ◽

Sky Conditions ◽

Type Classification ◽

Better Than

The classification of ground-based cloud images has received more attention recently. The result of this work applies to the analysis of climate change; a correct classification is, therefore, important. In this paper, we used 18 texture features to distinguish 7 sky conditions. The important parameters of two classifiers are fine-tuned in the experiment, namely, k-nearest neighbor (k-NN) and artificial neural network (ANN). The performances of the two classifications were compared. Advantages and limitations of both classifiers were discussed. Our result revealed that the k-NN model performed at 72.99% accuracy while the ANN model has higher performance at 86.93% accuracy. We showed that our result is better than previous studies. Finally, seven most effective texture features are recommended to be used in the field of cloud type classification.

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Ensemble Learning Technique for Cloud Classification

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3957.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2582-2587

Keyword(s):

Deep Learning ◽

Ensemble Learning ◽

Ensemble Methods ◽

Classification Performance ◽

Cloud Type ◽

Cloud Classification ◽

Learning Techniques ◽

Learning Technique ◽

Better Than

Automatic cloud classification is one of the important areas of remote sensing for metrological applications. Machine learning and deep learning techniques have been used for automatic classification of the cloud type. Several pretrained models are developed using convolutional neural network (CNN), which is part of deep learning. The classification performance of pretrained networks can be further improved using ensemble methods. Ensemble learning can perform better than single learner. In this paper, we proposed two different ensemble learning techniques: ensemble of CNN and ensemble of classifier. In first approach, CNN ensemble is performed, where the features extracted by two or more CNN are combined together using single classifier. The second method is to ensemble the predictions of different classifiers produced by a single or multiple CNN. The accuracy of cloud classification of the proposed methods has improved compared to without ensemble of pretrained networks

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