<title>Performance metrics for point target detection in consecutive frame IR imagery</title>

Abstract

In the companion paper, two algorithms for tracking point targets in consecutive frame staring IR imagery with evolving cloud clutter are described and compared by using representative example scenes. Here, our total data base of local airborne scenes with targets of opportunity are used for a more quantitative and comprehensive comparison. The use of real world data as well as our focus on temporal filtering over large number of consecutive frames triggered a search for more relevant metrics than those available. We present two new metrics which have most of the attributes sought. In each metric, gain is taken as a ratio of output to input signal to clutter. Maximum values rather than statistical measures are used for clutter. In the variation metric (VM), a temporal standard deviation for each pixel over 95 consecutive frames is computed and the maximum non-target result is taken as the input clutter. The input signal, a real target moving with sub-pixel velocity through sampled imagery, is estimated by a reference mean technique. Output signal and clutter are taken as maximum target and clutter affected pixels in algorithm filtered outputs. In the second metric, the use of an anti-median filter (AM) provides symmetric treatment of input and output as well as signal and clutter. The maximum target and non-target response to the AM filter on input frames and output frames defines the signal and clutter measures. Our set of real-world data is plotted as output versus input signal to clutter for each metric and each algorithm and the pros and cons of each metric is discussed. With either metric, the signal to clutter gain ratios are approximately 5 - 6 dB greater with the temporal filter algorithm than with the velocity filter algorithm.