30 April 2018 Confidence level estimation in multi-target classification problems
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Abstract
This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.
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Shi Chang, Shi Chang, Jason Isaacs, Jason Isaacs, Bo Fu, Bo Fu, Jaejeong Shin, Jaejeong Shin, Pingping Zhu, Pingping Zhu, Silvia Ferrari, Silvia Ferrari, } "Confidence level estimation in multi-target classification problems", Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 1062818 (30 April 2018); doi: 10.1117/12.2319988; https://doi.org/10.1117/12.2319988
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