Short-Term Forecasting of Cloud Images Using Local Features

Short-term forecasting of cloud distribution within a sequence of all-sky images is an important issue in meteorological area. In this work, a cloud image forecasting system is designed, which includes three steps---cloud detection, cloud matching and motion estimation. We treat cloud detection as a classification problem based on Linear Discriminant Analysis. During the matching, a set of Speed Up Robust Features (SURF) are extracted to represent the cloud, then clouds are matched by computing correspondences between SURF features. Finally, affine transform is applied to estimate the motion of cloud. This local features based method is capable of predicting the rotation and scaling of cloud, while the traditional method is only limited to translational motion. Objective evaluation results show higher accuracy of the proposed method compared with some other algorithms.