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Optical Engineering

Spatial filtering for detection of partly occluded targets
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Paper Abstract

A Bayesian approach for data reduction based on spatial filtering is proposed that enables detection of targets partly occluded by natural forest. The framework aims at creating a synergy between terrain mapping and target detection. It is demonstrates how spatial features can be extracted and combined in order to detect target samples in cluttered environments. In particular, it is illustrated how a priori scene information and assumptions about targets can be translated into algorithms for feature extraction. We also analyze the coupling between features and assumptions because it gives knowledge about which features are general enough to be useful in other environments and which are tailored for a specific situation. Two types of features are identified, nontarget indicators and target indicators. The filtering approach is based on a combination of several features. A theoretical framework for combining the features into a maximum likelihood classification scheme is presented. The approach is evaluated using data collected with a laser-based 3-D sensor in various forest environments with vehicles as targets. Over 70% of the target points are detected at a false-alarm rate of <1%. We also demonstrate how selecting different feature subsets influence the results.

Paper Details

Date Published: 1 April 2011
PDF: 14 pages
Opt. Eng. 50(4) 047201 doi: 10.1117/1.3560262
Published in: Optical Engineering Volume 50, Issue 4
Show Author Affiliations
Christina A. Grönwall, Swedish Defence Research Agency (Sweden)
Gustav Tolt, Swedish Defence Research Agency (Sweden)
Tomas Chevalier, Swedish Defence Research Agency (Sweden)
Håkan Larsson, Swedish Defence Research Agency (Sweden)

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