Expanding the dimensions of hyperspectral imagery to improve target detection

On-going research to improve hyperspectral target detection generally focuses on statistical detector performance, reduction of background or environmental contributions to at-sensor radiance, dimension reduction and many other mathematical or physical techniques. These efforts are all aimed at improving target identification in a single scene or data cube. This focus on single scene performance is driven directly by the airborne collection concept of operations (CONOPS) of a single pass per target location. Todays pushbroom and whiskbroom sensors easily achieve single passes and single collects over a target location. If multiple passes are flown for multiple collects on the same location, the time scale for revisit is several minutes. Emerging gimbaled hyperspectral imagers have the capability to collect multiple scans over the same target location in a time scale of seconds. The ability to scan the same location from slightly different collection geometries below the time scale of significant solar and atmospheric change forces us to reexamine the methods for target detection via the fundamental radiance equation. By expanding the radiance equation in the spatial and temporal dimensions, data from multiple hyperspectral images is used simultaneously for determining at-sensor radiance and surface leaving radiance with the ultimate goal of improving target detection. This research reexamines the fundamental radiance equation for multiple scan collection geometries expanding both the spatial and temporal domains. In addition, our assumptions for determining at-sensor radiance are revisited in light of the increased dimensionality. The expanded radiance equation is then applied to data collected by a gimbaled long wave infrared hyperspectral imager. Initial results and future work are discussed.