On the application of BEMD and Tamura textural feature for recognizing ground-based cloud

Ground-based cloud recognition plays an essential role for automatic cloud observation. In particular, the recognition of clouds is remarkably challenging because that the shape, size, and composition of cloud is extremely variable under different atmospheric conditions. A new method is proposed to extract texturral feature using Bidimensional Empirical Mode Decomposition(BEMD) and Tamura textural analysis.. Cloud was decomposed into several IMFs by BEMD. Radial basis function polynomial interpolation was applied to construct the envelope. Then the number of zero-crossing, means and standard deviation of the amplitude in each IMFs were selected as the eigenvector for training processing. And Tamura textural feature analysis was used to extract the feature of directionality. Characteristics of the sample database cloud was established by synthesizing the two normalized eigenvector. The same method was applied to the images to be identified, then the images were categorized compared with the eigenvector of sample database by the average sample method. The simulated experiments show that the ground-based cloud can be recognized effectively by new method.