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Abstract

A novel data processing technique is provided for detecting and locating a target from a plurality of two-dimensional images generated by an imaging sensor such as an imaging lidar system. The present invention processes this series of two dimensional images (made with one or more imaging detectors) in an optimal statistical fashion to reliably detect and locate targets. This invention is a process by which the images are mathematically modified to reduce the deleterious effects of noise and thereby provide the highest possible probability of detection while simultaneously maintaining a very low probability of false alarm. The data processing technique described herein also provides an estimate of the reliability of the detection, the target location and an output image to be displayed for visual confirmation and perhaps classification by the operator. The method of the present invention includes some or all of the following steps: noise reduction, spatial filtering, noise parameter extraction, asymmetric threshold detection, contrast stretching, localization, recognition, range or depth determination and subimage mosaic generation. The present invention is particularly well suited for processing two dimensional images of underwater targets generated by an imaging sensor located on an airborne platform whereby the underwater target is precisely and accurately detected, located and identified.

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First Claim