Conference Agenda

Detecting objects lying beneath a forest cover using SAR measurements represents a major challenge, due to the response of the overlying vegetation volume, wave attenuation caused by propagation through the forest canopy, and high-intensity scattering mechanisms occurring at the ground level. 3D polarimetric SAR imaging, through Polarimetric SAR Tomography (PolTomoSAR), represents an appealing solution to address these limitations, as it enables discriminating objects from their background by leveraging both polarimetric and spatial diversities. This work investigates two approaches based on PolTomoSAR processing, and adapted to different tomographic acquisition configurations, i.e. different vertical resolution and ambiguity compromises.

The first method relies on PolTomoSAR data that feature high vertical resolution and a wide unambiguous elevation range. Full-Rank polarimetric SAR tomographic focusing techniques are employed to isolate, with a high resolution, scattering sources located a few meters above the ground, and estimate their full-rank polarimetric responses. The concealed object detection is then conducted, based on polarimetric parameters provided by classical decomposition techniques. A simple detector, combining a few source descriptors, such as the polarimetric entropy and indicators of double-bounce scattering, as well as the elevation information, proves effective in identifying artificial objects embedded in dense, masking vegetation.

The second approach considers an extreme, but far less complex configuration, consisting solely of a two-image PolinSAR acquisition. Ground-notched InSAR processing is applied to suppress ground-scattering contributions, whose polarimetric and radiometric features may prevent the detection of objects, providing a filtered image representing a possibly ambiguous sampling of the scene reflectivity in the vertical direction. In the context of concealed object detection, the choice of the interferometric baseline separating the acquisition trajectories is crucial, as it balances the suppression of the forest canopy contribution and the preservation of responses from above-ground objects. Further discrimination is then carried out through a polarimetric analysis.

Both methods are applied to a 21-image fully polarimetric L-band data set, acquired by the DLR F-SAR sensor over Dornstetten, Germany. The study site consists of a mixed forest area containing several man-made objects, such as vehicles, containers, and corner reflectors, that are deployed both inside and outside the forest. Results show that combining spatial and polarimetric diversity modes allows both methods to successfully detect the different artificial objects in the scene, outside and below the forested areas.