The Geodesic Distance between $\mathcal{G}_I^0$ Models and its Application to Region Discrimination

TL;DR

This paper introduces a novel method using geodesic distance between $ ext{G}_I^0$ models for improved region discrimination in SAR imagery, enabling better edge detection and sample comparison.

Contribution

It proposes a new feature extraction technique based on geodesic distances between $ ext{G}_I^0$ models for SAR image analysis, addressing practical region discrimination tasks.

Findings

Effective edge detection between regions with different textures.

Quantitative dissimilarity measurement between SAR image samples.

Advantages over traditional stochastic distances.

Abstract

The $\mathcal{G}_I^0$ distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the geodesic distance as a measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive geodesic distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of (i)~identifying edges between regions with different texture, and (ii)~quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the geodesic distance when compared to stochastic distances.