Abstract
We propose an analytical bias correction for the maximum likelihood estimators of the \(\mathcal{G}_{I}^{0}\) distribution. This distribution is a very powerful tool for speckled imagery analysis, since it is capable of describing a wide range of target roughness. We compare the performance of the corrected estimators with the corresponding original version using Monte Carlo simulation. This second-order bias correction leads to estimators which are better from both the bias and mean square error criteria.
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We gratefully acknowledge partial financial support from CNPq, Brazil.
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Vasconcellos, K.L.P., Frery, A.C. & Silva, L.B. Improving estimation in speckled imagery. Computational Statistics 20, 503–519 (2005). https://doi.org/10.1007/BF02741311
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DOI: https://doi.org/10.1007/BF02741311