17 April 2006 Land cover mapping from remote sensing data
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
H. S. Lim, H. S. Lim, M. Z. MatJafri, M. Z. MatJafri, K. Abdullah, K. Abdullah, N. M. Saleh, N. M. Saleh, C. J. Wong, C. J. Wong, Sultan AlSultan, Sultan AlSultan, "Land cover mapping from remote sensing data", Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450P (17 April 2006); doi: 10.1117/12.665433; https://doi.org/10.1117/12.665433

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