Synthetic Aperture Radar (SAR) has been widely used as an important system for information extraction in remote sensing applications. Such microwave active sensors have as main advantages the following features: (i) their operation does not depend on sunlight, either of weather conditions and (ii) they are capable of providing high spatial image resolution.Measures of contrast are a powerful tool in image processing and analysis, e.g., in denoising, edge detection, segmentation, classification, parameter estimation, change detection, and feature selection.There are many approaches to solve such problems, but they are scattered in the literature. To make these literature more organized, AlejandroC. Frery from Laboratório de Computação Científica e Análise Numérica – LaCCAN, Universidade Federal de Alagoas recalls the main statistical properties of SAR data (intensity and fully polarimetric formats), estimation techniques, ways of measuring the contrast, and applications of such measures to practical and relevant problems. "Contrast," in this context, is a generic denomination for "difference."This work has been published in the "Synthetic Aperture Radar Technology" special issue of the 6th issue of Journal of Radars in 2019.Figure 1 shows the mind map that led the writing of this article, the arrows denote the model and/or technique used to tackle each problem. This article presents a unified notation and makes explicit connections between problems, models, techniques, and their implementation. It includes a section devoted to parameter estimation (Figure 2), with a focus on the specific problems of SAR data. Although this article does not bring new results, it may help researchers and practitioners to dive in this fertile area. The paper concludes with emerging topics that are starting to gain attention from the SAR Remote Sensing community.
Figure 1 Mind map of this review contents
Figure 2 Illustration of parameter estimation by distance minimization