The inverse gamma distributed texture is important for modeling compound-Gaussian clutter (e.g. for sea reflections), due to the simplicity of estimating its parameters. We develop maximum-likelihood (ML) and method of fractional moments (MoFM) estimates to find the parameters of this distribution. We compute the Cramer-Rao bounds (CRBs) on the estimate variances and present numerical examples. We also show examples demonstrating the applicability of our methods to real lake-clutter data. Our results illustrate that, as expected, the ML estimates are asymptotically efficient, and also that the real lake-clutter data can be very well modeled by the inverse gamma distributed texture compound-Gaussian model.
Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture
IEEE Transactions on Aerospace and Electronic Systems ; 43 , 2 ; 775-779
2007-04-01
963091 byte
Article (Journal)
Electronic Resource
English
Maximum Likelihood Shift Estimation Using High Resolution Polarimetric SAR Clutter Model
British Library Conference Proceedings | 2011
|