Unmixing Prior to Supervised Classification of Remotely Sensed Hyperspectral Images

Supervised classification of hyperspectral images is a very challenging task due to the generally unfavorable ratio between the number of spectral bands and the number of training samples available a priori, which results in the Hughes phenomenon. For this purpose, several feature extraction methods have been investigated in order to reduce the dimensionality of the data to the right subspace without significant loss of the original information that allows for the separation of classes. In this letter, we explore the use of spectral unmixing for feature extraction prior to supervised classification of hyperspectral data using support vector machines. The proposed feature extraction strategy has been implemented in the form of four different unmixing chains and evaluated using two different scenes collected by National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer. The experiments suggest competitive results but also show that the definition of the unmixing chains plays an important role in the final classification accuracy. Moreover, differently from most feature extraction techniques available in the literature, the features obtained using linear spectral unmixing are potentially easier to interpret due to their physical meaning.