Point target tracking in hyperspectral images

This research addresses the problem of tracking a moving point target from a time sequence of hyperspectral images. We focus on the detection of moving targets with staring technologies such as the ones used in space surveillance and missile tracking applications. In these applications, the images consist of targets moving at sub-pixel velocity in backgrounds which are influenced by both evolving clutter and noise. The demand for a low false alarm rate on one hand and a high probability of detection on the other makes the tracking a challenging task. The use of hyperspectral images should be superior to current technologies due to the benefit of simultaneously exploiting two target specific properties: the spectral target characteristics and the time dependent target behavior. We propose an algorithm which is performed in two steps. The first step is the transformation of each of the hyperspectral images forming the sequence into a two dimensional image using a known point target detection acquisition algorithm. In the second step, target detection and tracking is performed by the means of time domain processing. A match-filter based technique is developed for the hyperspectral image transformation; a variance-filter based algorithm is used to detect the presence of targets from the temporal profile of each pixel while suppressing clutter specific influences. We then show results obtained on real image sequences.