Tensor Ensemble of Ground-Based Cloud Sequences: Its Modeling, Classification, and Synthesis

Since clouds are one of the most important meteorological phenomena related to the hydrological cycle and affect Earth radiation balance and climate changes, cloud analysis is a crucial issue in meteorological research. Most researchers only consider the classification task of cloud images while less attention has been paid to the synthesis one. In addition, all the existing research on cloud identification from sky images is based on single cloud images. However, the cloud-measuring devices on the ground actually take one image of the clouds every few minutes and collect a series of cloud images. Thus, the existing methods neglect the temporal information exhibited by contiguous cloud images. To overcome this drawback, in this letter we treat ground-based cloud sequences (GCSs) as dynamic texture. We then propose the Tensor Ensemble of Ground-based Cloud Sequences (eTGCS) model which represents the ensemble of GCSs in a tensor manner. In the eTGCS model, all GCSs form a single tensor, and each GCS is a subtensor of the single tensor. There are two main characteristics of the eTGCS model: 1) All GCSs share an identical mode subspace, which makes the classification convenient, and 2) a new GCS can be synthesized as long as the parameters of the eTGCS model are used. Therefore, less storage space is required. Comprehensive experiments are conducted to prove the superiority of our eTGCS model. The classification accuracy achieves 92.31%, and the synthesized GCSs are similar to the original ones in visual appearance.