FLIR ATR using location uncertainty

A model-based FLIR ATR algorithm is described. It uti- lizes boundary contrast for target detection and recognition. Bound- ary contrast is related to the location uncertainty at target boundary points. A polygon model is used for deriving target centroid location uncertainty caused by the boundary point location uncertainty. The significance of the work lies in the sound mathematical models used in deriving the relationship between contrast and location uncer- tainty for the boundary points and the relationship between bound- ary point location uncertainty and centroid location uncertainty. Ex- periment results show significantly improved performance in detection, recognition, and localization. © 2000 SPIE and IS&T. (S1017-9909(00)01602-0) 1 Introduction In the ATR application, the sensory images are usually taken in uncontrolled environment where the conditions of the image forming process may vary greatly, e.g., in out- door scenes with unknown numbers and types of targets, complicated terrain background, significant amount of clut- ter, all kinds of weather conditions, different season of the year and different time of the day, occlusion and camou- flage of the targets, etc. The great complexity in the image forming process has made ATR a distinctively difficult problem where the input image is characterized by ex- tremely high variability in the content and quality. This is particularly true with the forward-looking infrared ~FLIR! imagery. Compared with the first generation sensors, the second generation FLIR sensors give much better image with higher resolution, higher signal-to-noise ratio, and higher stability. The better image quality, especially increased number of pixels on targets, reveals more structure of the target appearance in the image, and allows the design of knowledge- and model-based algorithms. The situation is further improved by dramatically improving computing power which strongly supports sophisticated algorithms. There has been significant improvement in the observed performance of the ATR systems since the late 1980's. Since the second generation FLIR sensor has ap- proached the optical limits of sensor resolution, 1 improve- ment in ATR performance should be expected mainly from advancement in ATR algorithm instead of from further im- proved image quality. Model-based approaches to the ATR problem are the most promising in developing high perfor- mance systems. The ability of low-level feature extraction algorithms is restricted by their limited information input from the local image neighborhood. High-level information can be very useful to overcome this restriction. This is typically done in the model-based approaches. In these approaches, either the target models are directly utilized in the low-level informa- tion extraction modules, or hard decisions about the infor- mation content in the imagery are avoided on low-level measurements until they are combined, via the use of target models, into medium- or high-level evidence. In doing this, weak but consistent evidence in the low quality imagery can be recovered with significantly improved reliability. In this article, we describe an algorithm that computes a target saliency measure by properly utilizing the target boundary contrast evidence. The two major pieces in devel- oping the algorithm are the development of the relationship between boundary contrast and boundary point location un- certainty and the development of the model for target cen- troid location uncertainty induced by the boundary point location uncertainty. In experiments, the developed algo- rithm shows improved detection performance and more im- pressive improvement in recognition performance. The lo- calization performance is also good.