Exploiting spectral content for image segmentation in GPR data
Published
Conference Paper
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. © 2011 SPIE.
Full Text
Duke Authors
Cited Authors
- Wang, PK; Morton, KD; Collins, LM; Torrione, PA
Published Date
- July 13, 2011
Published In
Volume / Issue
- 8017 /
International Standard Serial Number (ISSN)
- 0277-786X
International Standard Book Number 13 (ISBN-13)
- 9780819485915
Digital Object Identifier (DOI)
- 10.1117/12.884874
Citation Source
- Scopus