DocumentCode :
626229
Title :
Detection of Wavelengths for Hail Identification Using Satellite Imagery of Clouds
Author :
Ravinder, Anisha ; Reddy, P.K. ; Prasad, Narayan
Author_Institution :
Vardhaman Coll. of Eng., Hyderabad, India
fYear :
2013
fDate :
5-7 June 2013
Firstpage :
205
Lastpage :
211
Abstract :
Weather forecasting is a formidable challenge in the field of science as it depends on multiple parameters which are dynamic and chaotic. The rain, snow and hails are different climatic conditions that depend on the atmospheric parameters and are major forms of precipitation. Hailstorms are measured using traditional radar. Radar based hail measurements face major problems as the signals are weak and face attenuation issues with strong echoes. Hence, satellite or digital images are one of the efficient sources in the prediction of hail. In the process of image acquisition from the satellite imagery it would often find barriers like noise, burrs and so on, obscure or even cover the original image of an area or can reduce the image quality which include lot of noise. Therefore wavelet transform is used to enhance the image or to eliminate striping noise. They have advantages over traditional wavelet methods in analyzing physical situations where the signals are discontinuities. One of the wavelet transform used in the prediction of hail for a satellite or digital image is the haar wavelet transform. Differentiation between rain and hail depends on the square root balance sparsity norm threshold value obtained on compressing and de-noising the satellite image. The proposed model yields an average accuracy of 89.15 % in the identification of hail.
Keywords :
Haar transforms; atmospheric techniques; atmospheric waves; clouds; geophysical image processing; image denoising; meteorological radar; rain; remote sensing by radar; snow; wavelet transforms; weather forecasting; atmospheric parameters; climatic conditions; clouds satellite imagery; digital images; echoes; face attenuation issues; haar wavelet transform; hail identification; hail measurements; image acquisition process; image burrs; image quality; image striping noise; multiple parameters; precipitation forms; rain; satellite image denoising; satellite images; science field; snow; square root balance; traditional radar; wavelength detection; weather forecasting; Clouds; Feature extraction; Ice; Noise; Satellites; Storms; Wavelet transforms; Haar wavelet transform; Hail; clustering; image processing; remote sensing; satellite imagery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence, Communication Systems and Networks (CICSyN), 2013 Fifth International Conference on
Conference_Location :
Madrid
Print_ISBN :
978-1-4799-0587-4
Type :
conf
DOI :
10.1109/CICSYN.2013.14
Filename :
6571366
Link To Document :
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