Classification of hyperspectral data using support vector machine

Classification is one of the most important tasks for remote sensing image processing. Most of the existing supervised classification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. In addition, many methods are constrained by high data dimension of hyperspectral images. In this paper, a novel learning method, the support vector machine (SVM), is applied to hyperspectral data classification. This method does not suffer the limitations of data dimensionality and limited samples. The foundations of the SVM have been developed by Vapnik (1995) and are gaining popularity in field of machine learning due to many attractive features and promising empirical performance. In our experiment, the support vectors, which are critical for classification, are obtained by learning from the training samples. Choosing appropriate kernel function and suitable parameters, better classification results are obtained