  Forward-looking ground-penetrating radar (FLGPR) has recently been
investigated as a remote sensing modality for buried target detection (e.g.,
landmines). In this context, raw FLGPR data is beamformed into images and then
computerized algorithms are applied to automatically detect subsurface buried
targets. Most existing algorithms are supervised, meaning they are trained to
discriminate between labeled target and non-target imagery, usually based on
features extracted from the imagery. A large number of features have been
proposed for this purpose, however thus far it is unclear which are the most
effective. The first goal of this work is to provide a comprehensive comparison
of detection performance using existing features on a large collection of FLGPR
data. Fusion of the decisions resulting from processing each feature is also
considered. The second goal of this work is to investigate two modern feature
learning approaches from the object recognition literature: the
bag-of-visual-words and the Fisher vector for FLGPR processing. The results
indicate that the new feature learning approaches outperform existing methods.
Results also show that fusion between existing features and new features yields
little additional performance improvements.
