Ground-Based Cloud-Type Recognition Using Manifold Kernel Sparse Coding and Dictionary Learning. (16th October 2018)

• Record Type: Journal Article
• Authors: Luo, Qixiang; Zhou, Zeming; Meng, Yong; Li, Qian; Li, Miaoying
• Other Names: Bonafoni Stefania Academic Editor.
• Is Part Of: Advances in meteorology. Volume 2018(2018)
• Publication Date: 2018-10-16
• Subjects: Meteorology -- Periodicals; Meteorology; Periodicals; 551.505
• DOI: 10.1155/2018/9684206
• Languages: English
• ISSNs: 1687-9309

Abstract

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.