1 research outputs found
Parameter Estimation for the Single-Look 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 distribution is flexible enough to model
different degrees of texture in speckled data. It is indexed by three
parameters: , related to the texture, , a scale parameter, and
, the number of looks which is related to the signal-to-noise ratio. Quality
estimation of 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