Preliminary exploration of geometrical regularized SAR tomography

Abstract

Synthetic Aperture Radar Tomography (TomoSAR) is a popular technique in the SAR imaging community due to its ability to solve the problem of layover and obtain a three-dimensional model of observed scene. However, current TomoSAR imaging methods rely on estimating the reflectivity distribution in the elevation direction pixel by pixel based on multi-baseline observations. This approach requires tens or even hundreds of repeated-track observations to achieve satisfactory results, which limits its applicability in time-sensitive applications. With the knowledge that humans can infer three-dimensional structures from a single two-dimensional SAR image, we propose a geometrical regularized TomoSAR imaging approach that leverages three-dimensional clues. The approach first extracts geometrical structures, which are then used to estimate the expected scatterer distribution using the initial reconstructed point cloud. This reduces uncertainty in the scatterer’s position and allows for an adaptive restriction of the search range based on different metrics, such as signal-to-noise level. As a result, the proposed method yields better performance with fewer observations and lower signal-to-noise levels. Simulation results demonstrate the superior performance of our proposed method over classical TomoSAR imaging methods in several aspects. Additionally, experiments with our own multi-baseline systems show that the proposed method outperforms traditional approaches with respect to 3D entropy, and outliers are significantly restricted to the estimated geometrical structures, which confirms the effectiveness of our proposed method.