Unmixing Hyperspectral Skin Data using Non-Negative Matrix Factorization

The ability to accurately detect a target of interest in a hyperspectral imagery (HSI) is largely dependent on the spatial and spectral resolution. While hyperspectral imaging provides high spectral resolution, the spatial resolution is mostly dependent on the optics and distance from the target. Many times the target of interest does not occupy a full pixel and thus is concealed within a pixel, i.e. the target signature is mixed with other constituent material signatures within the field of view of that pixel. Extraction of spectral signatures of constituent materials from a mixed pixel can assist in the detection of the target of interest. Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding abundances from the mixture. In this paper, a framework based on non-negative matrix factorization (NMF) is presented, which is utilized to extract the spectral signature and fractional abundance of human skin in a scene. The NMF technique is employed in a supervised manner such that the spectral bases of each constituent are computed first, and then these bases are applied to the mixed pixel. Experiments using synthetic and real data demonstrate that the proposed algorithm provides an effective supervised technique for hyperspectral unmixing of skin signatures.