Comparison Of Imaging Infrared Detection Algorithms

This paper compares the performance of four candidate target detection algorithms. The best known of these, "Superslice," was developed at the University of Maryland in 1977-78.' The other three algorithms are the "Spoke Filter" developed by the Army Missile Command in Huntsville;2,3 the "contrast box" (CB), a new concept under development at Texas Instruments; and the Ford-Aerospace double-gated contrast filter.' As part of Texas Instruments development activity, CB algorithm performance was compared with the others. To do this meaningfully, all four concepts were tested using a common data base. The measure of detection performance was the total number of candidate targets handed off to the feature extractor and classifier to achieve a specified probability of including the actual target in the total. An ideal detection algorithm in terms of this measure would only need to hand off one target (assuming only one target in the FOV) to achieve a probability of 1. Other performance measures were input and output signal-to-noise ratios (SNR) and algorithm gain. The data base used in this investigation consisted of 256 independent IR images containing targets nominally varying between 5 and 500 pixels in size. The results presented not only illustrate how the algorithm performance depends on range to the target, but show that no one algorithm is best for all values of pixels on target. Of the four, however, the contrast box appears to provide the best overall performance.