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Proceedings Paper

High-spatial resolution land cover mapping using remotely sensed image
Author(s): H. S. Lim; S. AlSultan; M. Z. MatJafri; K. Abdullah; A. N. Alias; C. J. Wong; N. Mohd. Saleh
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Paper Abstract

We attempted to investigate the potential of using satellite image for acquiring data for remote sensing application. This study investigated the potential of using digital satellite image for land cover mapping over AlQasim, Saudi Arabia. Satellite digital imagery has proved to be an effective tool for land cover studies. Supervised classification technique (Maximum Likelihood, ML, Minimum Distance-to- Mean, MDM, Parallelepiped, P) techniques were used in the classification analysis to extract the thematic information from the acquired scenes. Besides that, neutral network also performed in this study. The accuracy of each classification map produced was validated using the reference data sets consisting of a large number of samples collected per category. The study revealed that the ML classifier produced better result. The best supervised classifier was chosen based on the highest overall accuracy and Kappa statistic. The results produced by this study indicated that land cover features could be clearly identified and classified into a land cover map. This study suggested that the land cover types of AlQasim, Saudi Arabia can be accurately mapped.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6977, Optical Pattern Recognition XIX, 69770S (17 March 2008); doi: 10.1117/12.777240
Show Author Affiliations
H. S. Lim, Univ. Sains Malaysia (Malaysia)
S. AlSultan, Qassim Univ. College of Agriculture (Saudi Arabia)
M. Z. MatJafri, Univ. Sains Malaysia (Malaysia)
K. Abdullah, Univ. Sains Malaysia (Malaysia)
A. N. Alias, Univ. Sains Malaysia (Malaysia)
C. J. Wong, Univ. Sains Malaysia (Malaysia)
N. Mohd. Saleh, Univ. Sains Malaysia (Malaysia)


Published in SPIE Proceedings Vol. 6977:
Optical Pattern Recognition XIX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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