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One of the biggest challenges in an underwater target detection is that, unlike land-based scenes, the observed spectrum of an underwater target is highly dependent on the particular background that is in the scene. In particular, the observed spectrum is determined by not only the target reflectance signature but also by the optical properties of the water in which it is situated, as well as the depth of the target. It follows that signature-based detection algorithms must be able to accommodate the wide variation of observed spectra that a single target may exhibit in nature, and at any depth. In this article, we present a general framework for underwater detection in hyperspectral remote sensing imagery that uses physics-based modeling to calculate the target space-the set of all possible observed spectra for the target in a given scene-and then uses nonlinear mathematical models to exploit the structure intrinsic to the target space in order to reduce dimensionality and greatly simplify the detection problem. We include a series of simulated target images that demonstrates the effectiveness of this approach for multiple targets and depths.

An Underwater Target Detection Framework for Hyperspectral Imagery