Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances
Faculty of Sciences. Physics
S.l. , 2012
IEEE IGARSS 2012, International Geoscience and Remote Sensing Symposium , Munich, 22-27 July 2012
University of Antwerp
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.