Shannon Entropy for the <inline-formula><tex-math notation="LaTeX">$\mathcal {G}^0_I$</tex-math></inline-formula> Model: A New Segmentation Approach

Abstract

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 GI0 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 GI0 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.

Keywords

• $\mathcal {G}^0_I$ distribution%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$\mathcal {G}^0_I$</tex-math> </inline-formula> distribution
• clustering model
• information theory (IT)
• stochastic entropy