Cloud classification of ground-based infrared images combining manifold and texture features

Luo, Qixiang; Yan, Meng; Liu, Lei; Zhao, Xiaofeng; Zhou, Zeming

doi:10.5194/amt-11-5351-2018

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…Gan et al [17] used duplex norm-bounded sparse coding to classify cloud type; they extracted local descriptors from an input cloud image and then formed a holistic representation leveraging normal-bounded sparse coding and max-pooling strategy. Luo et al [18] combined texture feature and manifold features; the manifold features extracted on symmetric positive define (SPD) matrix space that can describe the non-Euclidean geometric characteristics of the infrared images; then, used a support vector machine (SVM) classifier. Luo et al also [19] proposed manifold kernel sparse coding and dictionary learning with three steps: feature extraction, dictionary learning, and classification.…”

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confidence: 99%

A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets

2019

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Research on clouds has an enormous influence on sky sciences and related applications, and cloud classification plays an essential role in it. Much research has been conducted which includes both traditional machine learning approaches and deep learning approaches. Compared with traditional machine learning approaches, deep learning approaches achieved better results. However, most deep learning models need large data to train due to the large number of parameters. Therefore, they cannot get high accuracy in case of small datasets. In this paper, we propose a complete solution for high accuracy of classification of cloud image patches on small datasets. Firstly, we designed a suitable convolutional neural network (CNN) model for small datasets. Secondly, we applied regularization techniques to increase generalization and avoid overfitting of the model. Finally, we introduce a model average ensemble to reduce the variance of prediction and increase the classification accuracy. We experiment the proposed solution on the Singapore whole-sky imaging categories (SWIMCAT) dataset, which demonstrates perfect classification accuracy for most classes and confirms the robustness of the proposed model.

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confidence: 99%

A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets

2019

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“…In this paper, we extend our previous work (Luo et al, 2018), and propose an improved cloud type classification method based on RCovDs. The diagram is shown in Fig.…”

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confidence: 79%

“…gradient (Luo et al, 2018), mean grey value (Calbó and Sabburg, 2008;Liu et al, 2011), the census transform histogram (Xiao et al, 2016;Zhuo et al, 2014), edge sharpness (Liu et al, 2011), and features based on Fourier transform (Calbó and Sabburg, 2008). The textural features contain the scale invariant feature transform (SIFT) (Xiao et al, 2016), the grey level cooccurrence matrix (GLCM) (Cheng and Yu, 2015;Heinle et al, 2010;J.…”

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Improving Cloud Type Classification of Ground-Based Images Using Region Covariance Descriptors

Tang¹,

Yang²,

Zhou³

et al. 2020

Preprint

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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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“…In addition, sparse representation on SPD matrix manifolds has been applied to these areas to achieve better performances [22,23]. In spite of its effectiveness, the matrix manifolds method is seldom investigated to address the task of cloud classification [24].…”

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confidence: 99%

Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning

Luo

Zhou

Yan

et al. 2018

Advances in Meteorology

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Recognizing cloud type of ground-based images automatically has a great influence on the weather service but poses a significant challenge. Based on the symmetric positive definite (SPD) matrix manifold, a novel method named “manifold kernel sparse coding and dictionary learning” (MKSCDL) is proposed for cloud classification. Different from classical features extracted in the Euclidean space, the SPD matrix fuses multiple features and represents non-Euclidean geometric characteristics. MKSCDL is composed of three steps: feature extraction, dictionary learning, and classification. With the learned dictionary, the SPD matrix of the cloud image can be described with the sparse code. The experiments are conducted on two different ground-based cloud image datasets. Benefitting from the sparse representation on the Riemannian matrix manifold, compared to the recent baselines, experimental results demonstrate that MKSCDL possesses a more competitive performance on both grayscale and colour image datasets.

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Cloud classification of ground-based infrared images combining manifold and texture features

Cited by 12 publications

References 35 publications

A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets

A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets

Improving Cloud Type Classification of Ground-Based Images Using Region Covariance Descriptors

Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning

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