A Physical Approach for SAR Speckle Simulation: First Results

Abstract

In this paper a SAR simulator able to provide images presenting appropriate speckle statistics is introduced. The simulator is able to generate both Exponential (fully developed speckle) and K-distributed speckle statistics, according to surface and radar parameters. It is based on sound physical models for the evaluation of the number of equivalent scatterers per resolution cell. The proposed simulator requires as inputs the radar, orbital and surface parameters. The statistics relevant to each single equivalent scatterer are then effectively generated as a 2-D scale mixture of Gaussians and the global statistics of the return from each resolution cell are obtained as the coherent sum of all the scatterers contributions. The rationale of the proposed simulation framework is detailed and meaningful results regarding the analysis of the first simulated images are discussed.

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

“…In particular, the signal macroscopic behavior is described evaluating the mean square value of the field scattered from the plane facets locally approximating the considered surface, assuming knowledge of the microscopic roughness parameters (H and T) and the electromagnetic parameters (ε and σ) of the surface [31]. Conversely, the microscopic behavior, which determines the presence of speckle, is introduced via a statistical model: in this paper, we assume a fully developed speckle [32,33], and the amplitude-signal value obtained for each facet is multiplied by one specific realization of a Rayleigh random variable [30]. In Table 2, the parameters used for the simulation of Sentinel-1 and COSMO-SkyMed images are reported.…”

Time-domain Statistics of the Electromagnetic Bias in GNSS-Reflectometry

“…As for low-resolution SAR sensors (the case of GNSS-R instruments), the number of scatterers in the resolution cell is very large, 1 T the statistics of SAR images follow the predictions of the Rayleigh model, and the Exponential model matches well the actual data (e.g., [25]). In this study, we have captured not only the time-average statistics of the scattered signal, and its impact on the EM bias, but its temporal dependence as well.…”

“…The electromagnetic bias fluctuations with time are found to exhibit a non-Gaussian probability density function, so it does not reduce as the square root of the number of measurements being averaged. The computed electromagnetic bias values and their dependence with geophysical and geometric parameters are important to predict the electromagnetic bias, so as to correct it in upcoming GNSS-R altimetry missions [25], such as the European Space Agency GEROS experiment on board the International Space Station [26,27], and to assess its impact in data assimilation studies, such as the one being performed in the "GNSS-Reflectometry Assessment of Requirements and Consolidation of Retrieval Algorithms" study in support of the GEROS experiment.…”

Adaptive Restoration of Multispectral Datasets used for SVM classification

“…Their main goal is to improve the detection ratio for ship targets from SAR imaging mechanism. In Di Martino et al [2013] a framework is developed in order to simulate SAR images with speckle statistics. A so-called 'BiShrink' threshold as well as a novel remote sensing image denoising method is also presented in Li et al [2011].…”