Abstract
Abstract. The distribution and frequency of occurrence of different cloud types affect the energy balance of the Earth. Automatic cloud type classification of images continuously observed by the ground-based imagers could help climate researchers find the relationship between cloud type variations and climate change. However, by far it is still a 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 the Riemannian bag-of-feature (BoF) method. RCovDs model the correlations among different dimensional features, which 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 a BoF-based codebook. The multiclass support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets show that a very high prediction accuracy (more than 98 % on two datasets) can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods.
Highlights
Clouds affect the Earth’s climate by modulating Earth’s basic radiation balance (Hartmann et al, 1992; Ramanathan et al, 1989)
We extend our previous work (Luo et al, 2018), and propose an improved cloud type classification method based on region covariance descriptors (RCovDs)
This dataset was acquired by the whole-sky infrared cloudmeasuring system (WSIRCMS), which is located in Nanjing, China
Summary
Clouds affect the Earth’s climate by modulating Earth’s basic radiation balance (Hartmann et al, 1992; Ramanathan et al, 1989). (3) Different cloud types may have similar local characteristics; the global features need to be considered To address those issues, we utilize region covariance descriptors (RCovDs) to encode the features of the cloud image blocks, and with the aid of the bag-of-feature (BoF) method, we aggregate those local descriptors to obtain the global cloud image feature for cloud type classification. We extract multiple pixel-level features such as intensity, color and gradients from the cloud image blocks to form RCovDs. In the second step, RCovDs are encoded by the Riemannian BoF to output the histogram representation.
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