A novel ensemble support vector machine model for land cover classification

Nowadays, support vector machines are widely applied to land cover classification although this method is sensitive to parameter selection and noise samples. AdaBoost is an effective approach to find a highly accurate classifier by combining many weak and accurate classifiers. In this article, a novel ensemble support vector machine model that uses AdaBoost approach is proposed to mitigate the influence of noises and error parameters with focus on application on land cover classification. The key characteristics of this approach are that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noises and class noises to achieve an optimal clean set and (2) support vector machine classifiers, based on the particle swarm optimization algorithm, are seen to component classifiers. We then combined finally individual prediction through AdaBoost algorithm to induce the final classification results on this new training set. A set of experiments is conducted on land cover classification for testing the performance of the proposed algorithm. Experimental results show that the classification accuracy can be increased using our proposed learning model, which results in the smallest generalization error compared with the other learning methods