Abstract
Remote sensing images are an important source of information regarding the Earth surface. For many applications like geology, urban planning, forest and land cover/land use, the underlying information from such images is needed. Extraction of this information is usually achieved through a classification process which is one of the most powerful tools in digital image processing. Good classifier is required to extract the information in satellite images. Latest methods used for classification of pixels in multispectral satellite images are supervised classifiers such as Support Vector Machines (SVM), k-Nearest Number (K-NN) and Maximum Likelihood (ML) classifier. SVM may be one-class SVM or multi-class SVM. K-NN is simple technique in high-dimensional feature space. In ML classifier, classification is based on the maximum likelihood of the pixel. The performance metrics for these classifiers are calculated and compared. Totally 200 points have been considered for validation purpose.
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