| Paper: | SPTM-P3.11 (ICASSP 2003 Paper) | ||
| Session: | Time-Frequency Distributions | ||
| Time: | Wednesday, May 19, 09:30 - 11:30 | ||
| Presentation: | Poster (ICASSP 2003 Presentation) | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | ROBUST CLASSIFICATION OF SAR IMAGERY | ||
| Authors: | María Magdalena Lucini; University of Reading | ||
| Virginie Ruiz; University of Reading | |||
| Alejandro C. Frery; Universidade Federal de Pernambuco | |||
| Oscar H. Bustos; Universidad Nacional de Córdoba | |||
| Abstract: | In this work the GA0 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 GA0(a,g,n) law, where the parameter n is related to the speckle noise, and (a,g) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (a,g) 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 usinghard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed. | ||
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