Cloud Detection for FY Meteorology Satellite Based on Ensemble Thresholds and Random Forests Approach

Abstract

Cloud detection is the first step for the practical processing of meteorology satellite images, and also determines the accuracy of subsequent applications. For Chinese FY serial satellite, the National Meteorological Satellite Center (NSMC) officially provides the cloud detection products. In practical applications, there still are some misdetection regions. Therefore, this paper proposes a cloud detection method trying to improve NSMC’s products based on ensemble threshold and random forest. The binarization is firstly performed using ten threshold methods of the first infrared band and visible channel of the image, and the binarized images are obtained by the voting strategy. Secondly, the binarized images of the two channels are combined to form an ensemble threshold image. Then the middle part of the ensemble threshold image and the upper and lower margins of NSMC’s cloud detection result are used as the sample collection source data for the random forest. Training samples rely only on source image data at one moment, and then the trained random forest model is applied to images of other times to obtain the final cloud detection results. This method performs well on FY-2G images and can effectively detect incorrect areas of the cloud detection products of the NSMC. The accuracy of the algorithm is evaluated by manually labeled ground truth using different methods and objective evaluation indices including Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI) and the average and standard deviation of all indices. The accuracy results show that the proposed method performs better than the other methods with less incorrect detection regions. Though the proposed approach is simple enough, it is a useful attempt to improve the cloud detection result, and there is plenty of room for further improvement.