Hyperspectral data for KbSNMF

The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and be mixed in a macroscopic degree before arriving at the sensor element of the imaging spectrometer as mixed spectra. The paper titled Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence presents a novel blind HU algorithm, referred to as Kurtosis-based Smooth Nonnegative Matrix Factorization (KbSNMF) which incorporates a novel constraint based on the statistical independence of the probability density functions of endmember spectra. The proposed algorithm manages to outperform several state of the art NMF-based algorithms in terms of extracting endmember spectra from hyperspectral data. The attached datasets are utilized to reproduce the results presented in the above-mentioned paper.