Segmentation and classfication of hyperspectral images using Kendall Concordant Coefficient

As the abundant spectral information of hyperspectral image, traditional pixel-wise classification methods is time-consuming in hyperspectral images. And purely pixel-wise classification methods often ignore lots of space information. In this paper, we investigate the usage of Kendall Concordant Coefficient (KCC) for region-dependent segmentation of the original hyperspectral data cube. The KCC-based method could combine spectral and spatial information effectively, and it has strong robustness with low complexity because it is a nonparametric method. We conduct a series of experiments, and draw conclusions that KCC-based method could obtain better segmentation and classification results than purely pixel-wise methods.