Phase-correlation-based hyperspectral image classification using multiple class representatives obtained with k-means clustering

In this letter, a modification to a phase-correlation-(PC-)based supervised classification method for hyperspectral data is proposed. An adaptive approach using different numbers of multiple class representatives (CRs) extracted using PC-based k-means clustering for each class is compared with the use of selecting a small, pre-determined number of dissimilar CRs. PC is used as a distance measure in k-means clustering to determine the spectral similarity between each pixel and cluster centre. The number of representatives for each class is chosen adaptively, depending on the number of training samples in each class. Classification is performed for each pixel according to the maximum value of PCs obtained between test samples and the CRs. Experimental results show that the adaptive method gave the highest classification accuracy (CA). Experiments on the effect of reducing the size of the feature vectors found that CA increased as the feature vector decreased.