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%. © 2011 Springer-Verlag.
CITATION STYLE
Yang, L., Wang, Z. K., Wang, J., & Cui, W. T. (2011). Study of cloud-type recognition based on multi-class features. In Lecture Notes in Electrical Engineering (Vol. 122 LNEE, pp. 355–361). https://doi.org/10.1007/978-3-642-25553-3_44
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