A Monte-carlo Study of Classical Spectral Estimation of the Backscatter from Correlated K-distributed Speckled Sar Images

Some estimators for the spectral density of the return in Synthetic Aperture Radar (SAR) images are studied using Monte Carlo experiences. The spectral density is an important quantifier of the texture that, in turn, can be related to biophysical magnitudes. In this manner, it can be used to establish the kind of target being under observation in SAR images. . These images are contaminated by a particular kind of noise, called speckle, that does not obey the classical hypothesis of obeying the Gaussian law and entering the signal in an additive manner requiring, thus, a careful treatment. The departure from the Gaussian law will be modelled here by means of the K distribution. This law arises from certain (very realistic) hypothesis for the relationship between signal and noise. The empirical observation of structured data is modelled by the use of spatial correlation. There are two approaches to the problem of the presence of speckle noise, one being the use of techniques for its reduction (usually specially devised filters) and the other the proposal of methodologies that take its presence into account. Both approaches will be used here, to the problem of estimating the spatial correlation structure of the ground truth under the presence of speckle noise. The performance of these estimators will be assessed using Monte Carlo experiences, since the problem is analytically intractable.