A Probabilistic Method for the Classification of Hyperspectral Images

Abstract

In this study a supervised classification and dimensionality reduction method for hyperspectral images is proposed. For this purpose, using probabilistic principal component analysis (PPCA), dimensionality reduction is performed and a Gaussian mixture model (GMM) is built. Alongside this mixture model, spatial information is also included into the classification process by taking advantage of pixel neighborhoods.