KEYWORDS: LIDAR, 3D acquisition, 3D image processing, Expectation maximization algorithms, Spatial resolution, Algorithm development, Image resolution, Data modeling, Detection and tracking algorithms, Point spread functions
LADAR (LAser Detection and Ranging) systems can be used to provide 2-D and 3-D images of scenes. Generally,
2-D images possess superior spatial resolution without range data due to the density of their focal plane arrays.
A 3-D LADAR system can produce range to target data at each pixel, but lacks the 2-D system's superior spatial
resolution. It is the goal of this work to develop an algorithm using an Expectation Maximization approach for
fusing 2-D and 3-D LADAR data. The algorithm developed demonstrates both spatial and range resolution
improvement using simulated 2-D and 3-D LADAR data.
Laser detection and ranging (LADAR) systems can be used to provide 2-D and 3-D images of scenes. Generally, 2-D images possess superior spatial resolution but without range data due to the density of their focal plane arrays. A 3-D LADAR system can produce range-to-target data at each pixel but lacks the 2-D system's superior spatial resolution. We develop an algorithm using an expectation maximization approach to estimate both 3-D LADAR range and the bias associated with a 3-D LADAR system. The algorithm we develop demonstrates both spatial and range resolution improvement over standard interpolation techniques using both real and simulated 3-D and 2-D LADAR data.
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