Phase correlation based supervised classification of hyperspectral images using multiple class representatives

In this paper it is genuinely proposed to use a modified phase correlation (MPC) based supervised classification approach for hyperspectral images. The hyperspectral spectrum of each pixel is initially subsampled to gain robustness against noise and spatial variability, and phase correlation is applied to determine spectral similarity to class feature vectors. For this purpose it is required to obtain class feature vectors in the training phase. It is shown that the classification accuracy can be improved if multiple representative feature vectors are utilized for each class. These multiple representatives are selected from training data by finding training vectors of the same class that are less similar, so as to represent the class as good as possible with different representatives. Prediction is made according to the maximum value of the phase correlation results between new samples and the class representatives.