An efficient classifier design for remote sensing hyperspectral imagery

Among the various classifiers, the Support Vector Data Description (SVDD) is a well-known strong classifier since it uses nonparametric boundary approach that constructs the minimum hypersphere enclosing the target objects as much as possible. The SVDD has been used in many studies for classification, anomaly and target detection problems on airborne or spaceborne remote sensing hyperspectral images (HSI). In this paper, we have designed an efficient classifier using ensemble method with SVDD. As an ensemble approach, we have selected bagging technique with majority voting. To verify the performance improvement, we have tested the proposed classifier for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data. AVIRIS is a proven instrument in the realm of Earth remote sensing and has been flown on airborne platforms. The results show that the ensemble method based on bagging produces better performance than the conventional SVDD.