The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 835–842, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-835-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 835–842, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-835-2020

  21 Aug 2020

21 Aug 2020

CLOUD CLASSIFICATION FOR GROUND-BASED SKY IMAGE USING RANDOM FOREST

X. Wan and J. Du X. Wan and J. Du
  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Keywords: Cloud Classification, Total Sky Imager, Muti-feature, Random Forest

Abstract. The use of solar power as a renewable energy has grown rapidly over the last few decades. However, the amount of solar radiation reaching the ground vary significantly in the short term. Clouds are the main factor. In this paper, a novel cloud detection method for ground-based sky images is proposed. First, the multiple features from the sky images, including spectral, texture and colour features are combined into a feature set. Then, Random Forest with this feature set is used to classify different types of cloud and clear sky. The experimental results show that cumulus and cirrus clouds can be identified from sky images. Combined with random forest, three types of features and various feature combinations are used for cloud classification, respectively. The classification accuracy with multiple features is higher than that of single-type features and dual-type features.