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

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 propose...

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Bibliographic Details
Published in:Advances in meteorology Vol. 2018; no. 2018; pp. 1 - 9
Main Authors: Li, Qian, Meng, Yong, Zhou, Zeming, Luo, Qixiang, Li, Miaoying
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects:
Classification
Cloud classification
Cloud computing
Cloud types
Clouds
Coding
Colour
Datasets
Dictionaries
Euclidean geometry
Feature extraction
Fourier transforms
Fuses
Image classification
Learning
Machine learning
Manifolds
Manifolds (mathematics)
Mathematical analysis
Matrix methods
Measurement techniques
Meteorological services
Object recognition
ISSN:1687-9309, 1687-9317
Online Access:Get full text
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Summary: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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ISSN:1687-9309
1687-9317
DOI:10.1155/2018/9684206