Sub-aperture SAR Imaging with Uncertainty Quantification

TL;DR

This paper introduces a new sub-aperture SAR imaging method using sparse Bayesian learning to efficiently generate uncertainty-aware posterior densities, improving robustness and reducing parameter tuning compared to previous techniques.

Contribution

It presents a novel, efficient sub-aperture SAR imaging approach with uncertainty quantification that does not require user-defined parameters and is suitable for large-scale problems.

Findings

Efficient Bayesian method produces approximate posterior densities for sub-aperture images.

The approach reduces runtime and storage requirements for large SAR images.

Uncertainty quantification enhances robustness against non-isotropic scattering effects.

Abstract

In the problem of spotlight mode airborne synthetic aperture radar (SAR) image formation, it is well-known that data collected over a wide azimuthal angle violate the isotropic scattering property typically assumed. Many techniques have been proposed to account for this issue, including both full-aperture and sub-aperture methods based on filtering, regularized least squares, and Bayesian methods. A full-aperture method that uses a hierarchical Bayesian prior to incorporate appropriate speckle modeling and reduction was recently introduced to produce samples of the posterior density rather than a single image estimate. This uncertainty quantification information is more robust as it can generate a variety of statistics for the scene. As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient. Moreover, the method was not explicitly designed to mitigate the effects of the faulty isotropic scattering assumption. In this work we therefore propose a new sub-aperture SAR imaging method that uses a sparse Bayesian learning-type algorithm to more efficiently produce approximate posterior densities for each sub-aperture window. These estimates may be useful in and of themselves, or when of interest, the statistics from these distributions can be combined to form a composite image. Furthermore, unlike the often-employed lp-regularized least squares methods, no user-defined parameters are required. Application-specific adjustments are made to reduce the typically burdensome runtime and storage requirements so that appropriately large images can be generated. Finally, this paper focuses on incorporating these techniques into SAR image formation process. That is, for the problem starting with SAR phase history data, so that no additional processing errors are incurred.