Comparing Samples from the $\mathcal{G}^0$ Distribution using a Geodesic Distance
This work addresses the need for quantifying differences in SAR data samples for researchers in remote sensing and image analysis, but it appears incremental as it builds on existing tests.
The paper tackles the problem of comparing samples from the $\mathcal{G}^0$ distribution, used for SAR image modeling, by introducing a geodesic distance as a dissimilarity measure, and proposes three tests based on this distance with probability distributions estimated via permutation methods.
The $\mathcal{G}^0$ distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degree of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the $\mathcal{G}^0$ distribution using a Geodesic Distance (GD) as a measure of dissimilarity between models. The objective is quantifying the difference between pairs of samples from SAR data using both local parameters (scale and texture) of the $\mathcal{G}^0$ distribution. We propose three tests based on the GD which combine the tests presented in~\cite{GeodesicDistanceGI0JSTARS}, and we estimate their probability distributions using permutation methods.