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

Content Provider	IEEE Xplore Digital Library
Author	Mukhopadhyay, A. Shukla, B.P. Mukherjee, D. Chanda, B.
Copyright Year	2012
Description	Author affiliation: Atmospheric Sciences Division, Space Applications Centre, ISRO, Ahmedabad, India (Shukla, B.P.) \|\| ECS Unit, Indian Statistical Institute, Kolkata, India (Mukhopadhyay, A.; Mukherjee, D.; Chanda, B.)
Abstract	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.
Starting Page	1
Ending Page	5
File Size	308716
Page Count	5
File Format	PDF
ISBN	9781457715808
e-ISBN	9781457715839
DOI	10.1109/ICCCI.2012.6158795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Backpropagation Relaxation Satellites Activation Function Clouds Threshold-Logic Unit Neurons Thresholding Artificial neural networks Vectors Features Set Meteorology
Content Type	Text
Resource Type	Article

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