Abstract
In this paper we present a new methodology for automated target detection and identification in hyperspectral imagery. The standard paradigm for target detection in hyperspectral imagery is to run a detection algorithm, typically statistical in nature, and visually inspect each high-scoring pixel to decide whether it is a true detection or a false alarm. Detection filters have constant false alarm rates (CFARs) approaching 10−5, but these can still result in a large number of false alarms given multiple images and a large number of target materials. Here we introduce a new methodology for target detection and identification in hyperspectral imagery in conjunction with a data-driven target detection algorithm that shows promise for hard targets. The result is a greatly reduced false alarm rate and a practical methodology for aiding an analyst in quantitatively evaluating detected pixels.
Published Version
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