Title :
Feature extraction in the Hankel transform domain
Author :
Ciccarelli, F. ; Di Bisceglie, M. ; Galdi, C.
Author_Institution :
Nortel Networks, London, UK
Abstract :
Parameter estimation for the class of compound Gaussian random variables is considered in the more natural domain of the Hankel transform, where the expression of the probability density function appears generally more manageable. The estimation algorithm, based on the minimization of the integrated mean squared error between empirical and theoretical Hankel transform, has been tested for the case of K-distributed data using Monte Carlo simulation and some guidelines for the algorithm setup are derived
Keywords :
Gaussian processes; Hankel transforms; Monte Carlo methods; feature extraction; image recognition; least mean squares methods; parameter estimation; radar imaging; random processes; sonar imaging; Hankel transform domain; K-distributed data; Monte Carlo simulation; compound Gaussian random variables; estimation algorithm; feature extraction; integrated mean squared error; minimization; parameter estimation; probability density function; Backscatter; Clutter; Feature extraction; Intelligent networks; Maximum likelihood estimation; Minimization methods; Parameter estimation; Probability density function; Random variables; Synthetic aperture radar;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
DOI :
10.1109/IGARSS.2001.978106
