Enhanced Hyperspectral Unmixing via Non-Negative Matrix Factorization Incorporating the end Member Independence

Hyperspectral image analysis has been an efficacious method utilized in remote sensing applications as it has the ability to discover richer information due to the presence of multiple spectral bands representing data, as opposed to the three bands in tri-color images. However, due to the dismal spatial resolution, a single pixel may comprise of a mixture of spectra belonging to multiple surface materials. Hyperspectral unmixing strives to extract the constituent spectra and their mixing percentages in each pixel. Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF) are two prominent yet distinct methods used for hyperspectral unmixing. This paper proposes a novel method for hyperspectral unmixing, in which the fundamental notions of ICA are utilized to improve the accuracy of the standard NMF algorithm. This method was validated on standard and synthetic datasets and was observed to yield superior performance over the stand-alone implementations of ICA and NMF algorithms.