Tensor sparsity for classifying low-frequency ultra-wideband (UWB) SAR imagery
Although a lot of progress has been made over the years, one critical challenge still facing low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology is the discrimination of buried and obscured targets of interest from other natural and manmade clutter objects in the scene. The key issues are i) low-resolution SAR imagery for this frequency band, ii) targets of interests being typically small compared to the radar signal wavelengths, iii) targets having low radar cross sections (RCS), and iv) very noisy SAR imagery (e.g. target responses buried in responses from cluttered environment). In this paper, we consider the problem of discriminating and classifying buried targets of interest (buried metal and plastic mines, 155-mm unexploded ordinance [UXO], etc.) from other natural and manmade clutter objects (soda can, rocks, etc.) in the presence of noisy responses from the rough ground surfaces for low-frequency UWB 2-D SAR images. We generalize the traditional sparse representation-based classification (SRC) to a model with capability of using the information of the shared class, and implement multichannel classification problems by exploiting structures of sparse coefficients using various techniques. Here, we employ an electromagnetic (EM) SAR database generated using the finite-difference, time-domain (FDTD) software, which is based on a full-wave computational EM method.