13 August 1999 New automated terrain and feature extraction approach for the Predator UAV TESAR ATR system
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This paper describes a technique recently developed for target detection and false alarm reduction for the Predator unmanned aerial vehicle (UAV) tactical endurance synthetic aperture radar (TESAR) automatic target recognition (ATR) system. The approach does not attempt to label various objects in the SAR image (i.e., buildings, trees, roads); instead, it finds target-like characteristics in the image and compares their statistical/spatial relationship to larger structures in the scene. To do this, the approach merges the output of multiple CFAR (constant false alarm ratio) surfaces through a sequence of mathematical morphology tests. The output is further tested by a 'smart' clustering procedure, which performs an object- size test. With the use of these CFAR surfaces, a methodical sequence of morphological tests will find and retain large structures in the scene and eliminate cues that fall within these structures. The presence of supporting shadow downrange from the sensor is also used to eliminate objects with heights not typical to those of targets. Finally, a fast procedure performs a size test on elongated streaks. This procedure allows long objects to be smartly clustered as a single object while ensuring target proximity scenarios have no performance degradation. Application of this false alarm mitigator/detector to the Predator's SAR ATR algorithm suite produced a stunning reduction of one order of magnitude in the number of cues yielded by its baseline detector. This performance was consistent in scenes having natural and/or cultural clutter.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dalton S. Rosario, Dalton S. Rosario, } "New automated terrain and feature extraction approach for the Predator UAV TESAR ATR system", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357691; https://doi.org/10.1117/12.357691


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