16 September 2003 Estimating the ground plane in ladar three-dimensional imagery for target detection
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A common approach to detecting targets in laser radar (LADAR) 3-dimensional x, y and z imagery is to first estimate the ground plane. Once the ground plane is identified, the regions of interest (ROI) are segmented based on height above that plane. The ROIs can then be classifed based on their shape statistics (length, width, height, moments, etc.) In this paper, we present an empirical comparison of three different ground plane estimators. The first estimates the ground plane based on global constraints (a least median squares fit to the entire image). The second two are based on progressively more local constraints: a least median squares fit to each row and column the image, and a local histogram analysis of the re-projected range data. These algorithms are embedded in a larger system that first computes the target height above the ground plane and then recognizes the targets based on properties within the target region. The evaluation is performed using 98 LADAR images containing eight different targets and structured clutter (trees). Performance is measured in terms of percentage of correct detection and false alarm.
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Mark R. Stevens, Magnus Snorrason, Daniel W. Stouch, and Sengvieng Amphay "Estimating the ground plane in ladar three-dimensional imagery for target detection", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.485708; https://doi.org/10.1117/12.485708

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