A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images

Satellite image classification is a complex process that may be affected by many factors. This article addresses the problem of pixel classification of satellite images by a robust multiple classifier system that combines k-NN, support vector machine (SVM) and incremental learning algorithm (IL). The effectiveness of this combination is investigated for satellite imagery which usually have overlapping class boundaries. These classifiers are initially designed using a small set of labeled points. Combination of these algorithms has been done based on majority voting rule. The effectiveness of the proposed technique is first demonstrated for a numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on numeric data as well as two remote sensing data show that employing combination of classifiers can effectively increase the accuracy label. Comparison is made with each of these single classifiers in terms of kappa value, accuracy, cluster quality indices and visual quality of the classified images.

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