Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances

We present a new method for estimating the number of end-members present in a hyperspectral data set, based on the scaling behavior of nearest-neighbor distances. We demonstrate the method on artificial data, and show that it has a low dependence on the spectral dimensionality or the size of the data set. Furthermore, the proposed technique gives consistent results over different random instances of the data, indicated by a low standard deviation. On the AVIRIS Cuprite and Indian Pines data set, this technique yields results that are comparable to those obtained via other methods.