A nonlinear modeling framework for the detection of underwater objects in hyperspectral imagery

David B. Gillis

doi:10.1117/12.2228496

17 May 2016 A nonlinear modeling framework for the detection of underwater objects in hyperspectral imagery

David B. Gillis

Author Affiliations +

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 98401B (2016) https://doi.org/10.1117/12.2228496
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

The detection of underwater objects of interest (or targets) in hyperspectral imagery is a challenging problem, with a number of complications that are not present in land-based hyperspectral target detection. The main challenge in underwater detection is that, in contrast to land, where the observed spectrum of an associated target is largely independent of the surrounding background (e.g. the signature of a tank looks more or less the same whether it is on a road or in a field of grass), the observed spectrum of an underwater target is a highly nonlinear function of the background – that is, the optical properties of the water body that the object is submerged in, as well as the depth of the target. As a result, the same object in different types of water and/or at different depths will in general have very different observed spectral signatures. In this work, we present a general overview of the various challenges involved in underwater detection, and present a novel approach that fuses forward radiative-transfer modeling, ocean color predictions, and nonlinear mathematical techniques (manifold learning) to model both the background and target signature(s) and perform detection over a wide range of environmental conditions and depths.

Citation Download Citation

David B. Gillis "A nonlinear modeling framework for the detection of underwater objects in hyperspectral imagery", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401B (17 May 2016); https://doi.org/10.1117/12.2228496

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