Parallel processing method for airborne laser scanning data using a PC cluster and a virtual grid

Soo Hee Han, Joon Heo, Hong Gyoo Sohn, Kiyun Yu

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.

Original languageEnglish
Pages (from-to)2555-2573
Number of pages19
JournalSensors (Switzerland)
Volume9
Issue number4
DOIs
Publication statusPublished - 2009 Apr 1

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All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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