Elliptical symmetric distribution based maximal margin classification for hyperspectral imagery

It has been verified that hyperspectral data is statistically characterized by elliptical symmetric distribution. Accordingly, we introduce the ellipsoidal discriminant boundaries and present an elliptical symmetric distribution based maximal margin (ESD-MM) classifier for hypespectral classification. In this method, the characteristic of elliptical symmetric distribution (ESD) of hyperspectral data is combined with the maximal margin rule. This strategy enables the ESD-MM classifier to achieve good performance, especially when follows dimensionality reduction. Experimental results on real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrated that ESD-MM classifier has better performance than commonly used Bayes classifier, Fisher linear discriminant (FLD) and linear support vector machine (SVM).