Hyperspectral Image Classification Using Discrete Space Model and Support Vector Machines

In this letter, a novel method based on discrete space model (DSM) and support vector machines (SVMs) is proposed for hyperspectral image (HSI) classification. The DSM approach transforms continuous spectral signatures into discrete features and constructs a space model with the discrete features. Therefore, the classification capability of SVMs can be improved on account of the discrete feature space. Moreover, a composite kernel model is employed to take advantage of the spectral and spatial features among neighboring pixels. The proposed method is applied to real HSIs for classification. The experimental results confirm that the classification accuracy for the SVMs could be improved using the DSM method prior to classification.