In this work, we consider the development of algorithms for automated buried threat detection (BTD) using Ground Penetrating Radar (GPR) data. When viewed in GPR imagery, buried threats often exhibit hyperbolic shapes, and this characteristic shape can be leveraged for buried threat detection. Consequentially, many modern detectors initiate processing the received data by extracting visual descriptors of the GPR data (i.e., features). Ideally, these descriptors succinctly encode all decision-relevant information, such as shape, while suppressing spurious data content (e.g., random noise). Some notable examples of successful descriptors include the histogram of oriented gradient (HOG), and the edge histogram descriptor (EHD). A key difference between many descriptors is the precision with which shape information is encoded. For example, HOG encodes shape variations over both space and time (high precision); while EHD primarily encodes shape variations only over space (lower precision). In this work, we conduct experiments on a large GPR dataset that suggest EHD-like descriptors outperform HOG-like descriptors, as well as exhibiting several other practical advantages. These results suggest that higher resolution shape information (particularly shape variations over time) is not beneficial for buried threat detection. Subsequent analysis also indicates that the performance advantage of EHD is most pronounced among difficult buried threats, which also exhibit more irregular shape patterns.