Statistical models for target detection in infrared imagery

This paper illustrates a statistical model-based approach to the problem of target detection in a cluttered scene from long-wave infrared images, accommodating both unknown range to the target, unknown target location in the image, and unknown gain control settings on the imaging device. The philosophical perspective adopted emphasizes an iterative process of model creation and refinement and subsequent evaluation. The overarching theme is on the clear statement of all assumptions regarding the relationships between ground truth and corresponding imagery, the assurance that each admits quantifiable refutation, and the opportunity costs associated with their adoption for a particular problem.