24 January 2017 High-performance parallel approaches for three-dimensional light detection and ranging point clouds gridding
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Abstract
With the rapid advance of remote sensing technology, the amount of three-dimensional point-cloud data has increased extraordinarily, requiring faster processing in the construction of digital elevation models. There have been several attempts to accelerate the computation using parallel methods; however, little attention has been given to investigating different approaches for selecting the most suited parallel programming model for a given computing environment. We present our findings and insights identified by implementing three popular high-performance parallel approaches (message passing interface, MapReduce, and GPGPU) on time demanding but accurate kriging interpolation. The performances of the approaches are compared by varying the size of the grid and input data. In our empirical experiment, we demonstrate the significant acceleration by all three approaches compared to a C-implemented sequential-processing method. In addition, we also discuss the pros and cons of each method in terms of usability, complexity infrastructure, and platform limitation to give readers a better understanding of utilizing those parallel approaches for gridding purposes.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Permata Nur Miftahur Rizki, Permata Nur Miftahur Rizki, Heezin Lee, Heezin Lee, Minsu Lee, Minsu Lee, Sangyoon Oh, Sangyoon Oh, } "High-performance parallel approaches for three-dimensional light detection and ranging point clouds gridding," Journal of Applied Remote Sensing 11(1), 016011 (24 January 2017). https://doi.org/10.1117/1.JRS.11.016011 . Submission: Received: 11 October 2016; Accepted: 27 December 2016
Received: 11 October 2016; Accepted: 27 December 2016; Published: 24 January 2017
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