Shannon Entropy for the $\mathcal {G}^0_I$ Model: A New Segmentation Approach

Synthetic aperture radar (SAR) has been successfully used as a remote sensing tool. However, SAR images are contaminated by speckle noise and require specialized postprocessing procedures; e.g, tailored segmenters. The G
_I
⁰
distribution is a flexible model for SAR intensities because of its ability at describing heterogeneous clutters. Furthermore, applying information theory measures (e.g., entropy) to extract features in SAR imagery processing has achieved a prominent position. In this article, we derive both a closed-form expression for the G
_I
⁰
Shannon entropy and some of its mathematical properties. Consequently new entropy-based segmentation procedures for multidimensional SAR intensities-assuming independence or some dependence pattern-are also proposed. Finally, applications to real SAR imagery point out the proposed entropy-based segmenters can be more efficient than other well-defined methods, like the clustering by gamma mixture models.