  The horizontal orientation angle and vertical inclination angle of an
elongated subsurface object are key parameters for object identification and
imaging in ground penetrating radar (GPR) applications. Conventional methods
can only extract the horizontal orientation angle or estimate both angles in
narrow ranges due to limited polarimetric information and detection capability.
To address these issues, this letter, for the first time, explores the
possibility of leveraging neural networks with multi-polarimetric GPR data to
estimate both angles of an elongated subsurface object in the entire spatial
range. Based on the polarization-sensitive characteristic of an elongated
object, we propose a multi-polarization aggregation and selection neural
network (MASNet), which takes the multi-polarimetric radargrams as inputs,
integrates their characteristics in the feature space, and selects
discriminative features of reflected signal patterns for accurate orientation
estimation. Numerical results show that our proposed MASNet achieves high
estimation accuracy with an angle estimation error of less than 5{\deg}. The
promising results obtained by the proposed method encourages one to think of
new solutions for GPR-related tasks by integrating multi-polarization
information with deep learning techniques.
