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

Abstract

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.

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

“…The performance metrics are calculated using confusion matrix data that were published in previous research (in the case of Shi et al [24], the detail confusion matrix data was not provided; only accuracy is compared). The first three papers [20], [16], and [19] applied traditional machine learning approaches and they obtained accuracies of 91.1%, 95.1%, and 98.3%, respectively. The other papers [26], [27], and [24] applied deep learning with CNN approaches.…”

“…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. Wang et al [20] proposed a feature extraction method with a local binary pattern.…”

Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

“…It is extremely difficult to analyze cloud clusters in a small patch of sky, which makes it not widely used in the field of PV power generation. For the classification of part-sky ground-based cloud images, there have also been many research methods [17][18][19]. However, part-sky ground-based cloud images have a small field of view and cannot meet the large-scale PV power station requirements.…”