An automatic method to determine the coefficient of the composite kernel for hyperspectral image classification

Many studies [1]-[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of pattern recognition and machine learning, many researches [3]-[5] show that kernel method is computationally efficient, robust, and stable for pattern analysis. In this study, a novel method which automatically determines the coefficient of the composite kernel [5] that was proposed to join both spectral and spatial information for hyperspectral image classification via an optimail method for selecting an proper kernel function is proposed. The experimental results display the better performance of classification via the composite kernel with this novel method to determine the coefficient than using the RBF kernel function with 5-fold cross-validation method and optimal method to select proper parameter on the famous hyperspectral images, Washington DC Mall.