While it is universally recognized that image quality of a thermal sensor is a strong function of spatial uniformity, the metrics commonly used to assess performance do not adequately measure the effectiveness of non-uniformity correction (NUC). Image uniformity is generally not static, particularly if correction terms are updated intermittently (with periodic shuttering) or gradually (with scene-based NUC). Minimum Resolvable Temperature (MRT), the most prevalent test for characterizing overall imaging performance, is poorly suited for characterizing dynamic performance. The Triangle Orientation Discrimination (TOD) metric proposed by Bijl and Valeton, because of its short observation window, provides better capability for evaluating sensors that exhibit non-negligible uniformity drift. This paper compares the effectiveness of MRT and TOD for measuring dynamic performance. TOD measurements of a shutter-based thermal imager are provided immediately after shutter correction and 3 minutes later. The drift in TOD performance shows excellent corre lation to drift in system noise. BACKGROUNDJust five years ago, achieving noise equivalent temperature difference (NEdT) of 100 mK with an uncooled detector was considered a daunting challenge; today, several uncooled cameras tout sensitivity below 25 mK . Furthermore, pixel sizes have been continuously shrinking while focal plane array (FPA) formats have grown larger. As a result of the rapid progress, the uncooled infrared community places considerable attention on these three attributes -temporal NEdT, pixel size, and array format. Indeed, these parameters have emerged as de facto criteria by which uncooled systems are compared and judged. Unfortunately, these criteria completely neglect spatial uniformity (also called spatial noise) as a component of image quality. Consequently, the importance of effective NUC is often disregarded despite experimental evidence that spatial noise is in fact more detrimental to overall performance than temporal. 1 A more complete method of evaluating a Forward Looking Infrared (FLIR) system is MRT, which is commonly held as the best measure of overall imaging performance. One of the positive attributes of MRT is that it includes spatial noise in the assessment of performance. However, this advantage is undermined by the fact that no standard methods have been defined for adequately representing true NUC effectiveness when measuring MRT. For example, there are no definitive guidelines prescribing how often to update NUC terms when evaluating a system that periodically employs a shutter-based correction or how to handle performance drift between updates. Furthermore, MRT test conditions are poorly suited for assessing systems that use scenebased NUC, and there are no defined procedures for resolving this issue either. So while MRT does not completely disregard spatial noise, it does not always capture true performance in real-world conditions. Therefore, results can be very deceiving.Significant resources are being directed by the ...

No abstract

No abstract

No abstract

No abstract