Hyperspectral target detection using multiple platform cuing

Hyperspectral imaging has been demonstrated to achieve unresolved object detection through use of the spectral information. However, in many cases, these demonstrations have been in near ideal situations where the use of laboratory spectra with pristine data has lead to success. Complexities introduced in real-world situations such as a cluttered urban environment make successful detection challenging. One approach to improving performance is to use the synergistic effects of multiple sensors surveying a common area. These multiple sensors can be used to cue each other and enhance detection or tracking of objects. For maximum robustness, however one would want to minimize the complexity of processing algorithms such as those used to compensate for atmospheric and illumination effects. This paper investigates the limits of the use of spectra observed under one set of conditions to be used to detect an object under a different set of conditions. The results indicate good performance can be achieved across a reasonable range of illumination and viewing conditions.