The use of a range/passive-IR histogram as an approach to pixel-level fusion for cued target detection is discussed. Target detection algorithms for laser radar range imagery often use a number of computationally-intensive operations to locate targets in an image. These steps may include performing global geometric transforms, locating the ground plane, or applying size filters with associated rules. Each pixel in the image is processed multiple times, a time- consuming chore. An alternative to examining every pixel is to cue such detailed algorithms directly to an image location which is likely to contain a target. Cueing reduces the burden of searching or processing the entire image for regions of interest, greatly decreasing the number of computations needed for target detection. Cueing can be accomplished by combining registered laser radar range and passive-IR data. Since these data are taken simultaneously and are co-registered by Lincoln Laboratory's airborne laser radar, it is possible to combine them to form a powerful set of discriminants. There are two possible approaches to fusing the range and passive-IR data: (1) the domains can be processed for detections in two parallel streams and the resulting detection maps combined, or (2) the domains can be fused first and then processed as a single stream for detections. In the first method, sometimes called 'image-level' fusion, the processing algorithm for each domain can be optimized to obtain the best combination of detection and false alarm statistics. In the second method, the domains are combined at the pixel level, and the result is processed directly for target detection. The latter approach also reduces the number of computations since only data of interest to both domains is processed further. In this article, the multi-dimensional laser radar sensor and ongoing efforts to develop an automatic target recognition (ATR) system are described. The processing of pixel-registered laser radar range and passive-IR imagery for cued target detection using the range/passive-IR histogram is discussed. The authors present results for IR imagery with positive passive-IR target-to-background contrast to study the performance of the algorithm in real-world scenarios. These results are compared with a more typical detection method.
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