Subclutter target detection using sequences of thermal infrared multispectral imagery

Multivariate correlation techniques can be used to enhance target contrast in spectroscopic imagery. But in most cases the detectability of dim targets remains limited by residual background clutter. If, however, multiple-time measurements can be made, detection performance can be markedly enhanced by an integrated spectral/temporal technique that exploits the correlated nature of background spectral trajectories. We demonstrate the detection of extreme subpixel objects, such as is required by long-range remote sensing systems. We also show that the time intervals between data collections can be long. The confusing effects of natural background evolution-in temperature distribution or illumination-can be distinguished from anomalous changes. Data collected with longwave infrared point- and imaging-spectrometers have validated the concept.