eprintid: 441
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/04/41
datestamp: 2011-07-11
lastmod: 2013-07-01 13:19:00
status_changed: 2013-07-01 13:19:00
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Lizzi, Leonardo
creators_name: Viani, Federico
creators_name: Rocca, Paolo
creators_name: Oliveri, Giacomo
creators_name: Benedetti, Manuel
creators_name: Massa, Andrea
title: Three-Dimensional Real-Time Localization of Subsurface Objects: From Theory To Experimental Validation
ispublished: pub
subjects: TU
full_text_status: public
abstract: In the last years, significant efforts have been made to develop unsupervised systems able to detect landmines or unexploded ordnances for both military and civilian purposes. Several solutions have been proposed based on different methodologies to face this problem in a fast and effective way [1]. In such a framework, learning-by-examples (LBE) techniques [2][3] have demonstrated to be promising solutions able to enable detection procedures efficient in terms of both resolution and required time/computational resources. This paper is aimed at describing the detection problem as a three-dimensional classification process and analyzing its extension from theory to real experiments through a careful numerical analysis. Thanks to an integrated strategy based on a Support Vector Machine (SVM) classifier and a multi-resolution approach, a multi-resolution detection is obtained by means of an iterative zooming that considers only the regions characterized by an high probability to be occupied by the buried object. The arising time and computational saving allows the definition of an high-resolution map despite the complexity of the three-dimensional scenario at hand.
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] IEEE Trans. Geosci. Remote Sens., Special Issue on: “New Advances in Subsurface Sensing: Systems, modeling and Signal Processing,” vol. 32, Jun. 2001. [2] I. T. Rekanos, “Inverse scattering of dielectric cylinders by using radial basis function neural networks,” Radio Sci., vol. 36, no. 5, pp. 841-849, 2001. [3] A. Massa, A. Boni, and M. Donelli, “A classification approach based on SVM for electromagnetic subsurface sensing,” IEEE Trans. Geosci. Remote Sens., vol. 43, pp. 2084-2093, Sep. 2005. [4] V. Vapnick, Statistical Learning Theory, Wiley, New York, 1998.
citation:   Lizzi, Leonardo and Viani, Federico and Rocca, Paolo and Oliveri, Giacomo and Benedetti, Manuel and Massa, Andrea  (2011) Three-Dimensional Real-Time Localization of Subsurface Objects: From Theory To Experimental Validation.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/441/1/DISI-11-187.C180.pdf