Abstract. Automatic cloud type recognition of ground-based infrared images is still a challenging task. A novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. Compared with statistical features in Euclidean space, manifold features extracted on symmetric positive definite (SPD) matrix space can describe the non-Euclidean geometric characteristics of the infrared image more effectively. The proposed method comprises three stages: pre-processing, feature extraction and classification. Cloud classification is performed by a support vector machine (SVM). The datasets are comprised of the zenithal and whole-sky images taken by the Whole-Sky Infrared Cloud-Measuring System (WSIRCMS). Benefiting from the joint features, compared to the recent two models of cloud type recognition, the experimental results illustrate that the proposed method acquires a higher recognition rate with an increase of 2 %–10 % on the ground-based infrared datasets.

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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Abstract. Automatic cloud type recognition of ground-based infrared images is still a challenging task. A novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. Compared with statistical features in the Euclidean space, manifold features extracted on Symmetric Positive Definite (SPD) matrix space can describe the non-Euclidean geometric characteristics of the infrared image. The proposed method comprises three stages: pre-processing, feature extraction and classification. Cloud classification is performed by the Support Vector 10 Machine (SVM). The datasets are comprised of the zenithal and whole-sky images taken by the Whole-Sky Infrared CloudMeasuring System (WSIRCMS). Benefiting from the joint features, compared to the recent cloud type recognition methods, the experimental results illustrate that the proposed method acquires a higher recognition rate and exhibits a more competitive classification result on the ground-based infrared datasets.