Classification of Multispectral Data Using Support Vector Machines Approach for Density Estimation

In this paper, we present an approach for the classification of remote sensing multispectral data, which exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation. Extending the support vector machines to estimate multidimensional densities is explored. We use these estimates in the design and implementation of Bayes classification of multispectral Landsat data. Density estimation using SVM is compared with two traditional approaches, the Parzen window and k-NN approaches. Results on synthetic and real world remote sensing data show that the SVM estimates are more superior to the other methods in terms of accuracy, robustness and convergence speed.