Point target detection in IR image sequences: a hypothesis-testing approach based on target and clutter temporal profile modeling

Abstract

We approach the problem of point target detection in infrared image sequences by modeling the temporal behavior of clutter and targets on a single-pixel basis. These models, which are experimentally verified, are then used to develop a temporal likelihood-ratio test and derive the corresponding decision rule. We demonstrate the effectiveness of the technique by applying it to real infrared image sequences containing targets of opportunity and evolving cloud clutter. The physical models and resulting hypothesis-testing approach could also be applicable to other image-sequence-processing scenarios, using acquisition systems besides infrared imaging, such as the detection of small moving objects or structures in a biomedical or biological imaging scenario, or the detection of satellites, meteors, or other celestial bodies in night-sky imagery acquired using a telescope.