Feature extraction refers to the groups of techniques that, when applied to large dimensional and redundant data result in significant dimensionality reduction while preserving or even enhancing the information content. Among various techniques investigated for feature extraction, of new interest is Nonnegative Matrix Factorization (NMF). In NMF, it is assumed that the data is formed as a linear nonnegative combination of positive sources and the NMF solution recovers the original sources and the mixing matrix. In this paper, we first look at ways NMF can be applied for feature extraction in hyperspectral imagery a data known for large sizes and redundancy. While some of the associations are natural to linear mixing model (LMM - that assumes that hyperspectral images are formed as a linear mixture of endmember information), we also show NMF to be a slow method. To counter this, we investigate alternative solutions such as projected NMF approaches and provide an insight to how parallel implementations would contribute to speedup. Experimental results on various data show projected NMF outperforming regular NMF with parallel implementations providing a promising speedup advantage.