Exploiting spectral content for image segmentation in GPR data
2011
Ground-penetrating radar (GPR) sensors provide an effective means for detecting changes in the sub-surface
electrical properties of soils, such as changes indicative of landmines or other buried threats. However, most
GPR-based pre-screening algorithms only localize target responses along the surface of the earth, and do not
provide information regarding an object's position in depth. As a result, feature extraction algorithms are forced
to process data from entire cubes of data around pre-screener alarms, which can reduce feature fidelity and hamper
performance. In this work, spectral analysis is investigated as a method for locating subsurface anomalies in
GPR data. In particular, a 2-D spatial/frequency decomposition is applied to pre-screener flagged GPR B-scans.
Analysis of these spatial/frequency regions suggests that aspects (e.g. moments, maxima, mode) of the frequency
distribution of GPR energy can be indicative of the presence of target responses. After translating a GPR image
to a function of the spatial/frequency distributions at each pixel, several image segmentation approaches can be
applied to perform segmentation in this new transformed feature space. To illustrate the efficacy of the approach,
a performance comparison between feature processing with and without the image segmentation algorithm is
provided.
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