Accessibility navigation


Robust classification of SAR imagery

Lucini, M. M., Ruiz, V. F., Frery, A. C. and Bustos, O. H. (2003) Robust classification of SAR imagery. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol Vi, Proceedings - Signal Processing Theory and Methods. IEEE, pp. 557-560. ISBN 0780376633

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Abstract/Summary

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.

Item Type:Book or Report Section
Divisions:Faculty of Science
ID Code:14423
Uncontrolled Keywords:CLUTTER
Additional Information:Proceedings Paper IEEE International Conference on Acoustics, Speech, and Signal Processing APR 06-10, 2003 HONG KONG, PEOPLES R CHINA
Publisher:IEEE

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation