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Proceedings Paper

Mirage: a visible signature evaluation tool
Author(s): Joanne B. Culpepper; Alaster J. Meehan; Q. T. Shao; Noel Richards
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Paper Abstract

This paper presents the Mirage visible signature evaluation tool, designed to provide a visible signature evaluation capability that will appropriately reflect the effect of scene content on the detectability of targets, providing a capability to assess visible signatures in the context of the environment. Mirage is based on a parametric evaluation of input images, assessing the value of a range of image metrics and combining them using the boosted decision tree machine learning method to produce target detectability estimates. It has been developed using experimental data from photosimulation experiments, where human observers search for vehicle targets in a variety of digital images. The images used for tool development are synthetic (computer generated) images, showing vehicles in many different scenes and exhibiting a wide variation in scene content. A preliminary validation has been performed using k-fold cross validation, where 90% of the image data set was used for training and 10% of the image data set was used for testing. The results of the k-fold validation from 200 independent tests show a prediction accuracy between Mirage predictions of detection probability and observed probability of detection of r(262) = 0:63, p < 0:0001 (Pearson correlation) and a MAE = 0:21 (mean absolute error).

Paper Details

Date Published: 5 October 2017
PDF: 20 pages
Proc. SPIE 10432, Target and Background Signatures III, 104320G (5 October 2017); doi: 10.1117/12.2277555
Show Author Affiliations
Joanne B. Culpepper, Defence Science and Technology Group (Australia)
Alaster J. Meehan, YTEK Pty Ltd. (Australia)
Q. T. Shao, Thermovoltaic Technologies Pty Ltd. (Australia)
Noel Richards, YTEK Pty Ltd. (Australia)


Published in SPIE Proceedings Vol. 10432:
Target and Background Signatures III
Karin U. Stein; Ric Schleijpen, Editor(s)

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