2018/06/04 by Daniel Reichman, Daniël Reichman, Reichman, Daniel +4
Computer Science · Engineering · #Advanced SAR Imaging Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geophysical Methods and Applications #Microwave Imaging and Scattering Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1806.01349
5 pages, 6 figures, letter
openalex publication_date 2018/06/04 · arxiv created 2018/10/02 · arxiv updated 2018/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Substantial research has been devoted to the development of algorithms that automate buried threat detection (BTD) with ground penetrating radar (GPR) data, resulting in a large number of proposed algorithms. One popular algorithm GPR-based BTD, originally applied by Torrione et al., 2012, is the Histogram of Oriented Gradients (HOG) feature. In a recent large-scale comparison among five veteran institutions, a modified version of HOG referred to here as "gprHOG", performed poorly compared to other modern algorithms. In this paper, we provide experimental evidence demonstrating that the modifications to HOG that comprise gprHOG result in a substantially better-performing algorithm. The results here, in conjunction with the large-scale algorithm comparison, suggest that HOG is not competitive with modern GPR-based BTD algorithms. Given HOG's popularity, these results raise some questions about many existing studies, and suggest gprHOG (and especially HOG) should be employed with caution in future studies.