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 ddimensional 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 selected machine learning algorithms.

2-D wavelet-based spectra with application in analysis of geophysical images

NICOLIS, Orietta;
2008-01-01

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 ddimensional 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 selected machine learning algorithms.
2008
Nicolis, Orietta; Garutti, Claudio; Vidakovic, Brani
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/543
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