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17 March 2008 High-spatial resolution land cover mapping using remotely sensed image
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
H. S. Lim, S. AlSultan, M. Z. MatJafri, K. Abdullah, A. N. Alias, C. J. Wong, and N. Mohd. Saleh "High-spatial resolution land cover mapping using remotely sensed image", Proc. SPIE 6977, Optical Pattern Recognition XIX, 69770S (17 March 2008); https://doi.org/10.1117/12.777240
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