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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-189
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-189
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Aug 2020

07 Aug 2020

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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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