Prediction of meteorological images based on relaxation labeling and artificial neural network from a given sequence of images

In this paper an algorithm to predict the spectral signature of the short-term evolution of cloud formations using image sequences acquired from ISRO meteorological satellite (Kalpana-1) is described. The proposed algorithm consists of four steps: first step perform image processing activities (thresholding and relaxation); the second step is dedicated to determination of neural net for each pixel in each and every images. The third and fourth step consists of a novel neural network based training and prediction respectively. The main goal of this work is to maximize the prediction accuracy. Various kind of predictions are made depending upon number of feature vectors and number of net used. Mean Square Error is used to evaluate the performance of the neural net and PSNR is used to judge the accuracy of predicted image. Some experimental results obtained by using real image sequences acquired from ISRO meteorological satellite are shown that are extreamly encouraging.