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Improving cloud type classification of ground-based images using region covariance descriptors
Atmospheric Measurement Techniques
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 andoi:10.5194/amt-14-737-2021 fatcat:psm4fnl7aze6xajf5oa3jyrr7y