FLIR range performance predictions using synthetic imagery

Abstract

Traditional FLIR performance analysis uses analytic models to predict sensor performance characteristics such as modulation transfer function (MTF) and minimum resolvable temperature (MRT). These characteristics are then used in conjunction with empirical criterion such as the Johnson cycle criteria to predict the performance of observers using the modeled sensor. In general, such an analysis suffers from inadequate descriptions of the effects of the background and incomplete descriptions of the observer detection mechanism. Accurate predictions of field performance in a particular setting require the expensive collection of imagery for metric analysis or perception tests. In this paper, an image-based approach is investigated. Using an advanced FLIR simulation, synthetic image sets are generated under controlled conditions. Using these image sets, image metrics are calculated and predictions of target detectability are made using a contrast-to-clutter model and a computational vision model. These predictions are compared to results obtained using a traditional range performance analysis from the MRT based ACQUIRE model. An assessment of the advantages and disadvantages of the image-based approach is given.

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

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