Neural network-based cloud classification on satellite imagery using textural features

Automatic cloud classification of satellite imagery can be of great help to meteorological studies. A neural network-based cloud classification system is developed and introduced in this paper. Several image transformation schemes such as Wavelet Transform (WT) and Singular Value Decomposition (SVD) are used to extract the salient textural feature of the data and is compared them with those of the well-known Gray-leve Go-occurrence Matrix (GLCM) approach. Two different neural network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) are chosen and examined in this paper. The performance of the proposed cloud classification system is benchmarked on the Geostationary Operational Environmental Satellite (GOES) 8 data set and promising results have been achieved.