Forward-looking ground penetrating radar (FLGPR) is a remote sensing modality that has been investigated for buried threat detection. The FLGPR considered in this work consists of a sensor array mounted on the front of a vehicle, which inspects an area in front of the vehicle as it moves down a lane. The FLGPR collects data using a stepped frequency approach, and the received radar data is processed by filtered backprojection to create images of the subsurface. A large body of research has focused on developing effective supervised machine learning algorithms to automatically discriminate between imagery associated with target and non-target FLGPR responses. An important component of these automated algorithms is the design of effective features (e.g., image descriptors) that are extracted from the FLGPR imagery and then provided to the machine learning classifiers (e.g., support vector machines). One feature that has recently been proposed is computed from the magnitude of the two-dimensional fast Fourier transform (2DFFT) of the FLGPR imagery. This paper presents a modified version of the 2DFFT feature, termed 2DFFT+, that yields substantial detection performance when compared with several other existing features on a large collection of FLGPR imagery. Further, we show that using partial least-squares discriminative dimensionality reduction, it is possible to dramatically lower the dimensionality of the 2DFFT+ feature from 2652 dimensions down to twenty dimensions (on average), while simultaneously improving its performance.
The forward-looking ground penetrating radar (FLGPR) is a remote sensing modality that has recently been investigated
for buried threat detection. The FLGPR considered in this work uses stepped frequency sensing followed by filtered
backprojection to create images of the ground, where each image pixel corresponds to the radar energy reflected from
the subsurface at that location. Typical target detection processing begins with a prescreening operation where a small
subset of spatial locations are chosen to consider for further processing. Image statistics, or features, are then extracted
around each selected location and used for training a machine learning classification algorithm. A variety of features
have been proposed in the literature for use in classification. Thus far, however, predominantly hand-crafted or
manually designed features from the computer vision literature have been employed (e.g., HOG, Gabor filtering, etc.).
Recently, it has been shown that image features learned directly from data can obtain state-of-the-art performance on a
variety of problems. In this work we employ a feature learning scheme using k-means and a bag-of-visual-words model
to learn effective features for target and non-target discrimination in FLGPR data. Experiments are conducted using
several lanes of FLGPR data and learned features are compared with several previously proposed static features. The
results suggest that learned features perform comparably, or better, than existing static features. Similar to other feature
learning results, the features consist of edges or texture primitives, revealing which structures in the data are most useful
for discrimination.
Forward-looking ground penetrating radar (FLGPR) is a remote sensing modality that has recently been investigated for buried threat detection. FLGPR offers greater standoff than other downward-looking modalities such as electromagnetic induction and downward-looking GPR, but it suffers from high false alarm rates due to surface and ground clutter. A stepped frequency FLGPR system consists of multiple radars with varying polarizations and bands, each of which interacts differently with subsurface materials and therefore might potentially be able to discriminate clutter from true buried targets. However, it is unclear which combinations of bands and polarizations would be most useful for discrimination or how to fuse them. This work applies sparse structured basis pursuit, a supervised statistical model which searches for sets of bands that are collectively effective for discriminating clutter from targets. The algorithm works by trying to minimize the number of selected items in a dictionary of signals; in this case the separate bands and polarizations make up the dictionary elements. A structured basis pursuit algorithm is employed to gather groups of modes together in collections to eliminate whole polarizations or sensors. The approach is applied to a large collection of FLGPR data for data around emplaced target and non-target clutter. The results show that a sparse structure basis pursuits outperforms a conventional CFAR anomaly detector while also pruning out unnecessary bands of the FLGPR sensor.
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