Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition

All-sky images derived from ground-based imaging equipment have become an important means of recognizing and quantifying cloud information. Accurate cloud detection is a prerequisite for obtaining important cloud information from an all-sky image. Existing cloud segmentation algorithms can achieve high accuracy. However, for different scenes, such as completely cloudy with obscured sun and partly cloudy with unobscured sun, the use of specific algorithms can further improve segmentation. In this study, a hybrid cloud detection algorithm based on intelligent scene recognition (HCD-ISR) is proposed. It uses suitable cloud segmentation algorithms for images in different scenes recognized by ISR, so as to utilize the various algorithms to their full potential. First, we developed an ISR method to automatically classify the all-sky images into three scenes. In scene A, the sky is completely clear; in scene B, the sky is partly cloudy with unobscured sun; and in scene C, the sun is completely obscured by clouds. The experimental results show that the ISR method can correctly identify 93% of the images. The most suitable cloud detection algorithm was selected for each scene based on the relevant features of the images in that scene. A fixed thresholding (FT) method was used for the images in scene C. For the most complicated scene, that is, scene B, the clear-sky background difference (CSBD) method was used to identify cloud pixels based on a clear-sky library (CSL). The images in the CSL were automatically filtered by ISR. Compared to FT, adaptive thresholding (AT), and CSBD methods, the proposed HCD-ISR method has the highest accuracy (95.62%). The quantitative evaluation and visualization results show that the proposed HCD-ISR algorithm makes full use of the advantages of different cloud detection methods, and is more flexible and robust.