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• W56769319 abstract "Abstract : This dissertation addresses the problem of detecting buried explosive threats (i.e. landmines and improvised explosive devices) with ground-penetrating radar (GPR) and hyperspectral imaging (HSI) across widely-varying environmental conditions. Automated detection of buried objects with GPR and HSI is particularly difficult due to the sensitivity of sensor phenomenology to variations in local environmental conditions. Past approaches have attempted to mitigate the effects of ambient factors by designing statistical detection and classification algorithms to be invariant to such conditions. An alternative approach to improving detection performance is to consider exploiting differences in sensor behavior across environments rather than mitigating them, and treat changes in the background data as a possible source of supplemental information for the task of classifying targets and non-targets. This approach is referred to as context-dependent learning. Although past researchers have proposed context-based approaches to detection and decision fusion, the definition of context used in this work differs from those used in the past. In this work, context is motivated by the physical state of the world from which an observation is made, and not from properties of the observation itself. The proposed context-dependent learning technique therefore utilized additional features that characterize soil properties from the sensor background, and a variety of nonparametric models were proposed for clustering these features into individual contexts. The number of contexts was assumed to be unknown a priori, and was learned via Bayesian inference using Dirichlet process priors. The learned contextual information was then exploited by an ensemble on classifiers trained for classifying targets in each of the learned contexts." @default.
• W56769319 created "2016-06-24" @default.
• W56769319 creator A5065276045 @default.
• W56769319 date "2012-01-01" @default.
• W56769319 modified "2023-09-27" @default.
• W56769319 title "Nonparametric Bayesian Context Learning for Buried Threat Detection" @default.
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