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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Aug 2020

07 Aug 2020

Review status
This preprint is currently under review for the journal AMT.

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

Yuzhu Tang1,3, Pinglv Yang2, Zeming Zhou2, Jianyu Chen3, Delu Pan3, and Xiaofeng Zhao2 Yuzhu Tang et al.
  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
  • 2College of Meteorology and Oceanology, National University of Defence Technology, Nanjing, 211101, China
  • 3State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou, 310012, China

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.

Yuzhu Tang et al.

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Yuzhu Tang et al.

Yuzhu Tang et al.


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