Study of Cloud-Type Recognition Based on Multi-class Features

According to changeability of cloud, cloud-type recognition was primarily based on single-class feature in previous papers which was restricted to a certain degree. A set of features describing the color, texture as well as the shape features were extracted, then the shape and texture features combination methods were discussed. Here Gray-level co-occurrence matrix(GLCM) and Gabor wavelet transform based texture features and Zernike moment based shape features were combined, then support vector machine (SVM) was employed to recognize cloud-type. Experimental results showed that the correct recognition rates of altocumulus, cirrus, clear, cumulus and stratus were improved significantly, with the average recognition rate of 88.6%, and clear sky and stratus’s recognition rate of 100%.