Ground-based Cloud Classification Using Multiple Random Projections

Ground-based cloud classification plays an essential role in meteorological research and has received great concern in recent years. In this paper, a novel algorithm named multiple random projections (MRP) is proposed for ground-based cloud classification. The proposed algorithm uses an ensemble approach of MRP to obtain an optimized textons. Based on the textons, discriminative features can be obtained for classification. A series of experiments on two ground-based cloud databases (Kiel and IapCAS-E) are conducted to evaluate the efficiency of our proposed method. In addition, three current state-of-the-art methods, which include Patch, PCA, single random projection (SRP), are selected for comparison purpose. The experimental results show that our MRP method can achieved the best classification performance.