Concealed Object Discrimination in Forested Areas Using PolTomoSAR with Various Baseline Configurations

Abstract. Detecting objects hidden beneath a forest cover with Synthetic Aperture Radar (SAR) is challenging due to strong vegetation scattering, canopy attenuation, and ground returns. This work investigates two methods for detecting concealed targets using Polarimetric tomographic SAR (PolTomoSAR). The first approach exploits full-rank polarimetric tomographic focusing to achieve high-resolution separation of scattering sources and estimate their polarimetric responses. Target detection is then carried out using descriptors derived from decomposition techniques, such as the polarimetric entropy, and double-bounce scattering intensity, enabling the identification of man-made objects embedded within a dense vegetation layer. The second approach considers a compact configuration using only two interferometric SAR (InSAR) images. Coherent ground-notching suppresses the dominant ground scattering contribution, while preserving responses from above-ground scatterers. It is demonstrated that the baseline value plays a significant role in the detection process, and an optimum value is selected. Both methods are evaluated using L-band data set acquired by the DLR F-SAR over Dornstetten, Germany. Results demonstrate successful detection of concealed objects for varying baseline configurations.