An Information-Theoretic Detector for Multiple Scatterers in SAR Tomography

TL;DR

This paper introduces an information-theoretic detector for identifying multiple scatterers in SAR Tomography, combining hypothesis testing and compressive sensing to improve urban 3D reconstruction and deformation monitoring.

Contribution

It presents a novel one-stage adaptive architecture for multiple hypothesis testing in SAR Tomography using information theory and compressive sensing techniques.

Findings

Effective detection of multiple scatterers demonstrated on simulated data.

Improved urban 3D reconstruction accuracy shown with real SAR data.

Outperforms existing methods in scatterer detection reliability.

Abstract

Persistent scatterer interferometry and Synthetic Aperture Radar (SAR) Tomography are powerful tools for the detection and time monitoring of persistent scatterers. They have been proven to be effective in urban scenarios, especially for buildings and infrastructures 3-D reconstruction and monitoring of deformation. In urban areas, occurrence of layover leads to the presence of multiple contributions within the same image pixel from scatterers located at different heights. In the context of SAR Tomography, this problem can be addressed by considering a multiple hypothesis test to detect the presence of feasible multiple scatterers [1][2]. In the present paper, we consider this problem in the framework of the information theory and exploit the theoretical tool, developed in [3], to design a one-stage adaptive architecture for multiple hypothesis testing problems in the context of SAR Tomography. Moreover, we resort to the compressive sensing approach for the estimation of the unknown parameters under each hypothesis. This architecture has been verified on both simulated as well as real data also in comparison with suitable counterparts.