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Proceedings Paper

Denoising and hyperbola recognition in GPR data
Author(s): Vittorio Belotti; Fabio Dell'Acqua; Paolo Gamba
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Paper Abstract

The automatic analysis of Ground Penetrating Radar (GPR) images is an interesting topic in remote sensing image processing, since it involves the use of pre-processing, detection and classification tools with the aim of near-real time or at least very fast data interpretation. However, actual chains of preprocessing tools for GPR images do not consider usually denoising, essentially because most of the successive data interpretation is based on single radar trace analysis. So, no speckle noise analysis and denoising has been attempted, perhaps assuming that this point is immaterial for the following interpretation or detection tools. Instead, we expect that speckle denoising procedures would help. In this paper we address this problem, providing a detailed and exhaustive comparison of many of the statistical algorithms for speckle reduction provided in literature, i.e. Kuan, Lee, Median, Oddy and wavelet filters. For a more precise comparison, we use the Equivalent Number of Look (ENL), the Variance Ratio (VR). Moreover, we validate the denoising results by applying an interpretation step to the pre-processed data. We show that a wavelet denoising procedure results in a large improvement for both the ENL and VR. Moreover, it also allows the neural detector to individuate more targets and less false positive in the same GPR data set.

Paper Details

Date Published: 28 January 2002
PDF: 11 pages
Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454141
Show Author Affiliations
Vittorio Belotti, Univ. degli Studi di Pavia (Italy)
Fabio Dell'Acqua, Univ. degli Studi di Pavia (Italy)
Paolo Gamba, Univ. degli Studi di Pavia (Italy)

Published in SPIE Proceedings Vol. 4541:
Image and Signal Processing for Remote Sensing VII
Sebastiano Bruno Serpico, Editor(s)

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