2-D Wavelet-Based Spectra with Application in Analysis of Geophysical Images

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Nicolis, Orietta
Garutti, Claudio
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Wallace H. Coulter Department of Biomedical Engineering
The joint Georgia Tech and Emory department was established in 1997
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http://hdl.handle.net/1853/25838
Abstract
We propose a wavelet-based spectral method for estimating the (directional) Hurst parameter in isotropic and anisotropic non-stationary fractional Gaussian fields. The method can be applied to self-similar images and, in general, to d- dimensional data that scale. In the application part, we consider denoising of 2-D fractional Brownian fields and the classification of the clouds/temperature satellite images. In the first application, we use Bayesian inference calibrated by information from the wavelet-spectral domain to separate the signal, in this case the 2-D Brownian field, and the noise. For the classification of geophysical images we first estimate directional Hurst exponents and use them as an input to standard machine learning algorithms
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2006
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Technical Report
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