M.R. Mustapha, H.S. Lim, M.Z. Mat Jafn, S. Syahreza
The classification accuracy obtained from the classification of satellite images using pixel-by-pixel conventional methods can be improved if the contextual information is considered during the classification process. This study presents a comparison of frequency-based contextual and maximum likelihood approaches to identify the land cover patterns in arid environment of multi-spectral images collected by SPOT-2 satellite. In image classification, in order to obtain a good result, not only the image resolution is considered but the selection of the classifier to be used during decision making process is important as well. In present study, two classifiers have been experimented in order to evaluate their performances which is Maximum Likelihood classifier representing as conventional method whereas contextual approach representing as advanced method. Conventional classification methods commonly cannot handle the complex landscape environment in the image. The result of each method has often a salt and pepper appearances which is a main characteristic of mis classification. It seems clear that information from neighbouring pixels should increase the discrimination capabilities of the pixel based measured and thus, improve the classification accuracy and the interpretation efficiency. This information is referred to as spatial contextual information. The experimental results indicated that frequency-based contextual algorithm with 83.7% overall accuracy and 0.693 Kappa coefficient is more reliable than the maximum likelihood algorithm with 72.1% and 0.527 overall accuracy and Kappa coefficient, respectively. The high value of the frequency-based contextual classification is due to the fact that this algorithm could overcome the mixed pixel problem and reduce the speckle error in the image significantly. © 2011 Asian Network for Scientific Information.
School of Physics, Universiti Sains Malaysia, 11800 Penang, Malaysia; Department of Physics, Faculty of Mathematics and Natural Sciences, Syiah Kuala University, Banda Aceh, 23111, Indonesia
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