Parameter Estimation for the Single-Look $\mathcal{G}^0$ Distribution
The statistical properties of Synthetic Aperture Radar (SAR) image texture reveals useful target characteristics. It is well-known that these images are affected by speckle, and prone to contamination as double bounce and corner reflectors. The $\mathcal{G}^0$ distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: $\alpha$, related to the texture, $\gamma$, a scale parameter, and $L$, the number of looks which is related to the signal-to-noise ratio. Quality estimation of $\alpha$ is essential due to its immediate interpretability. In this article, we compare the behavior of a number of parameter estimation techniques in the noisiest case, namely single look data. We evaluate them using Monte Carlo methods for non-contaminated and contaminated data, considering convergence rate, bias, mean squared error (MSE) and computational cost. The results are verified with simulated and actual SAR images.
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