Hyperspectral detection algorithms: operational, next generation, on the horizon

Abstract

The multiband target detection algorithms implemented in hyperspectral imaging systems represent perhaps the most successful example of image fusion. A core suite of such signal processing methods that fuse spectral channels has been implemented in an operational system; more systems are planned. Stricter performance requirements for future remote sensing applications will be met by evolutionary improvements on these techniques. Here we first describe the operational methods and then the related next generation nonlinear methods, whose performance is currently being evaluated. Next we show how a "dual" representation of these algorithms can serve as a springboard to a radically new direction in algorithm research. Using nonlinear mathematics borrowed from machine learning concepts, we show how hyperspectral data from a high-dimensional spectral space can be transformed onto a manifold of even higher dimension, in which robust decision surfaces can be more easily generated. Such surfaces, when projected back into spectral space, appear as enveloping blankets that circumscribe clutter distributions in a way that the standard, covariance-based methods cannot. This property may permit the design of extremely low false-alarm rate solutions to remote detection problems