Exploring limits in hyperspectral unresolved object detection
Hyperspectral imaging systems have been shown to enable unresolved object detection through enhanced spectral characteristics of the data. Robust detection performance prediction tools are desirable for many reasons including optimal system design and operation. The research described in this paper explores the general understanding of system factors that limit detection performance. Examples are shown for detectability limits due to target subpixel fill fraction, sensor noise, and scene complexity.