  Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture
radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or
mine, has been successfully demonstrated recently. Despite promising recent
progress, a significant open challenge is to distinguish obscured targets from
other (natural and manmade) clutter sources in the scene. The problem becomes
exacerbated in the presence of noisy responses from rough ground surfaces. In
this paper, we present three novel sparsity-driven techniques, which not only
exploit the subtle features of raw captured data but also take advantage of the
polarization diversity and the aspect angle dependence information from
multi-channel SAR data. First, the traditional sparse representation-based
classification (SRC) is generalized to exploit shared information of classes
and various sparsity structures of tensor coefficients for multi-channel data.
Corresponding tensor dictionary learning models are consequently proposed to
enhance classification accuracy. Lastly, a new tensor sparsity model is
proposed to model responses from multiple consecutive looks of objects, which
is a unique characteristic of the dataset we consider. Extensive experimental
results on a high-fidelity electromagnetic simulated dataset and radar data
collected from the U.S. Army Research Laboratory side-looking SAR demonstrate
the advantages of proposed tensor sparsity models.
