BAND SELECTION BASED GAUSSIAN PROCESSES FOR HYPERSPECTRAL REMOTE SENSING IMAGES CLASSIFICATION

Classification of Hyperspectral remote sensing images is an important research direction. Hyperspectral remote sensing images have high dimension and nonlinear property. Band selection is often adopted firstly to reduce computational cost and accelerate knowledge discovery of subsequent classification and analysis. Furthermore, Hyperspectral images often contain some uncertainty brought by mixed pixels. We proposed a new band selection based Gaussian processes method to solve these problems. Our method is a Bayesian kernel-based nonlinear method, so it is suitable for nonlinear data classification and it can reduce the uncertainty by computation of posterior label probabilities. Experiment results show that our method is very good at classification of Hyperspectral remote sensing images with respect to classification accuracy and stability.