Enhanced GPR target classification by Compressed Sensing and radar polarimetry

Polarimetric technology has been one of the most important advances in microwave remote sensing during recent decades. The Entropy-α decomposition, which is a type of polarimetric analysis technique, has been common for terrain and land-use classification in polarimetric synthetic aperture radar. For certain scenarios, this kind of processing is also of interest for the interpretation of Ground Penetrating Radar (GPR) measurements. However, due to the limitation of the imaging resolution, which affects the performance of the polarimetric analysis, the classification of subsurface targets is not as reliable as in the original SAR scenario. In this paper we will apply blind support space (BSS) based compressed sensing (CS) to improve the performance of the polarimetric classification of GPR data. This is beneficial, because the support space is usually unknown and location-dependent. We demonstrate the feasibility of this approach based on full polarimetric data measured during a campaign in a controlled environment. The results from these measurements show that a BSS and CS based polarimetric GPR provides significant advantages in targets classification compared to standard GPR methods.