The use of LIDAR (Light Imaging, Detection and Ranging) data for detailed terrain mapping and object recognition is becoming increasingly common. While the rendering of LIDAR imagery is expressive, there is a need for a comprehensive performance metric that presents the quality of the LIDAR image. A metric or scale for quantifying the interpretability of LIDAR point clouds would be extremely valuable to support image chain optimization, sensor design, tasking and collection management, and other operational needs. For many imaging modalities, including visible Electro-optical (EO) imagery, thermal infrared, and synthetic aperture radar, the National Imagery Interpretability Ratings Scale (NIIRS) has been a useful standard. In this paper, we explore methods for developing a comparable metric for LIDAR. The approach leverages the general image quality equation (IQE) and constructs a LIDAR quality metric based on the empirical properties of the point cloud data. We present the rationale and the construction of the metric, illustrating the properties with both measured and synthetic data.
Ye Duan, John M. Irvine, Hua-mei Chen, Genshe Chen, Erik Blasch, and James Nagy, "Feasibility of an interpretability metric for LIDAR data," Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 1064506 (Presented at SPIE Defense + Security: April 16, 2018; Published: 22 May 2018); https://doi.org/10.1117/12.2305960.
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