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24 January 2017 High-performance parallel approaches for three-dimensional light detection and ranging point clouds gridding
Permata Nur Miftahur Rizki, Heezin Lee, Minsu Lee, Sangyoon Oh
Author Affiliations +
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) 1931-3195/2017/$25.00 © 2017 SPIE
Permata Nur Miftahur Rizki, Heezin Lee, Minsu Lee, and 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
Received: 11 October 2016; Accepted: 27 December 2016; Published: 24 January 2017
JOURNAL ARTICLE
19 PAGES


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Cited by 3 scholarly publications.
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KEYWORDS
LIDAR

3D modeling

Data modeling

Parallel computing

Parallel processing

Clouds

Computer programming

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