Browse > Article
http://dx.doi.org/10.7848/ksgpc.2017.35.3.187

Parallel Processing of K-means Clustering Algorithm for Unsupervised Classification of Large Satellite Imagery  

Han, Soohee (Dept. of Geoinformatics Engineering, Kyungil University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.35, no.3, 2017 , pp. 187-194 More about this Journal
Abstract
The present study introduces a method to parallelize k-means clustering algorithm for fast unsupervised classification of large satellite imagery. Known as a representative algorithm for unsupervised classification, k-means clustering is usually applied to a preprocessing step before supervised classification, but can show the evident advantages of parallel processing due to its high computational intensity and less human intervention. Parallel processing codes are developed by using multi-threading based on OpenMP. In experiments, a PC of 8 multi-core integrated CPU is involved. A 7 band and 30m resolution image from LANDSAT 8 OLI and a 8 band and 10m resolution image from Sentinel-2A are tested. Parallel processing has shown 6 time faster speed than sequential processing when using 10 classes. To check the consistency of parallel and sequential processing, centers, numbers of classified pixels of classes, classified images are mutually compared, resulting in the same results. The present study is meaningful because it has proved that performance of large satellite processing can be significantly improved by using parallel processing. And it is also revealed that it easy to implement parallel processing by using multi-threading based on OpenMP but it should be carefully designed to control the occurrence of false sharing.
Keywords
K-means Clustering; Parallel Processing; Unsupervised Classification; Satellite Imagery;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Clematis, A., Mineter, M., and Marciano, R. (2003), High performance computing with geographical data, Parallel Computing, Vol. 29, Issue 10, pp. 1275-1279.   DOI
2 Healey, R., Dowers, S., Gittings, B., and Mineter, M.J. (1997), Parallel Processing Algorithms for GIS, CRC Press, UK.
3 Koo, I.H. (2012), High-speed Processing of Satellite Image Using GPU, Master's thesis, Chungnam National University, Daejeon, Korea, pp. 28-42. (in Korean with English abstract)
4 Lee, K., Jo, M., and Lee, W. (2016), Parallel processing of satellite images using CUDA library: focused on NDVI calculation, Journal of the Korean Association of Geographic Information Studies, Vol. 19, No. 3, pp. 29-42. (in Korean with English abstract)   DOI
5 MacQueen, J. (1967), Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, University of California Press, Berkeley, California, USA, 21 June-18 July, pp. 281-297.
6 OpenMP ARB (2016), The OpenMP API specification for parallel programming, OpenMP ARB, http://www. openmp.org (last date accessed: 25 May 2017).
7 Plaza, A.J. and Chang, C. (2007), High Performance Computing in Remote Sensing, CRC Press, UK.
8 Sugumaran, R., Hegeman, J.W., Sardeshmukh, V.B., and Armstrong, M.P. (2015), Processing remote-sensing data in cloud computing environments, In: Thenkabail, P.S. (ed.), Remotely Sensed Data Characterization, Classification, and Accuracies, CRC Press, UK, pp. 549-558.
9 Wang, P., Wang, J., Chen, Y., and Ni, G. (2013), Rapid processing of remote sensing images based on cloud computing, Future Generation Computer Systems, Vol. 29, Issue 8, pp. 1963-1968.   DOI
10 Han, S.H., Heo, J., Sohn, H.G., and Yu, K. (2009), Parallel processing method for airborne laser scanning data using a PC cluster and a virtual grid, Sensors, Vol. 9, Issue 4, pp. 2555-2573.   DOI
11 Wikipedia (2017a), Amdahl's law, Wikimedia Foundation, Inc., https://en.wikipedia.org/wiki/Amdahl%27s_law (last data accessed: 25 May 2017).
12 Wikipedia (2017b), False sharing, Wikimedia Foundation, Inc., https://en.wikipedia.org/wiki/False_sharing (last data accessed: 25 May 2017).
13 Wikipedia (2017c), Parallel computing, Wikimedia Foundation, Inc., https://en.wikipedia.org/wiki/Parallel_computing (last data accessed: 25 May 2017).
14 Yang, C. and Hung, C. (2000), Parallel computing in remote sensing data processing, Proceedings of the 21st Asian Conference on Remote Sensing, ACRS, 4-8 December, Taipei, Taiwan, unpaginated CD-ROM.