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Proceedings Paper

Land cover mapping from remote sensing data
Author(s): H. S. Lim; M. Z. MatJafri; K. Abdullah; N. M. Saleh; C. J. Wong; Sultan AlSultan
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Paper Abstract

Remote sensing data have been widely used for land cover mapping using supervised and unsupervised methods. The produced land cover maps are useful for various applications. This paper examines the use of remote sensing data for land cover mapping over Saudi Arabia. Three supervised classification techniques Maximum Likelihood, ML, Minimum Distance-to-Mean, MDM, and Parallelepiped, P were applied to the imageries to extract the thematic information from the acquired scene by using PCI Geomatica software. Training sites were selected within each scene. This study shows that the ML classifier was the best classifier and produced superior results and achieved a high degree of accuracy. The preliminary analysis gave promising results of land cover mapping over Saudi Arabia by using Landsat TM imageries.

Paper Details

Date Published: 17 April 2006
PDF: 8 pages
Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450P (17 April 2006); doi: 10.1117/12.665433
Show Author Affiliations
H. S. Lim, Univ. Sains Malaysia (Malaysia)
M. Z. MatJafri, Univ. Sains Malaysia (Malaysia)
K. Abdullah, Univ. Sains Malaysia (Malaysia)
N. M. Saleh, Univ. Sains Malaysia (Malaysia)
C. J. Wong, Univ. Sains Malaysia (Malaysia)
Sultan AlSultan, Remote Sensing Ctr. of Environment (Saudi Arabia)


Published in SPIE Proceedings Vol. 6245:
Optical Pattern Recognition XVII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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