Data collected by hand-held and vehicular-mounted ground penetrating radar (GPR) devices can be viewed as a sequence of A-scans grouped into frames. Sequences of frames create a three-dimensional representation of the collected area. The ground structure within this area includes: the ground layer, sub-surface layers, explosive hazards, and non-explosive (clutter) objects. In previous work, we found a wireframe view of two-dimensional layers within the three-dimensional volume. In this work, we analyze how to use image segmentation techniques to identify and view the entire three-dimensional volume of layers and objects found in the data. First, image value and contour differentiate structure. Then, we employ a multi-stage process of image segmentation, clustering analysis using Competitive Agglomeration, and optimizing a Markov Random Field (MRF). The collection sequence of hand-held system data may not always fit into a grid representation. Therefore, segmentation techniques are modified from a frame by frame grid sequence to a nearest neighborhood of three-dimensional regions. Results are displayed in an interactive viewing tool that displays each stage of the process and represents the scene elements identified.
Peter J. Dobbins and Joseph N. Wilson, "Layer tracking using image segmentation," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820C (Presented at SPIE Defense + Security: April 10, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2262565.
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