A new hierarchical classifier for hyperspctral data with similar spectrum

To improve the classification accuracy of image in which many classes have the similar spectrum, this paper presents a new hierarchical classification scheme for hyperspectral images (HSI). The Spectral Angle Mapping (SAM) is firstly used to combine the similar classes into large classes. Next the hierarchical classifier classifies the image with large classes and then divides every large class into normal classes further. For every large class, the most suitable feature extraction method and classifier are chosen empirically. Meanwhile, a new band selection is proposed to help every large class find the bands which can better reflect the differences of classes according to the characteristic of spectrum. Experiments are conducted on a 103-band ROSIS image of University of Pavia. The experimental results show that the hierarchical classifier is better than the single classifier used only once. Especially when the spectra of the given classes are so similar that the traditional classifiers couldn´t divide them thoroughly, the proposed classifier can make it. Moreover, the hierarchical classifier can do more efficiently because it excludes some redundant bands and concentrates on the bands with slight differences.