A number of non invasive methods have been developed to characterize parameters in near-surface skin tissue; however, the work has usually been concerned with using either spectral or spatial information. This motivated our study in which both spatial and spectral data are used to extract features for characterizing the spatial distribution of near-surface oxygen saturation. This paper addresses combined physical and statistical models to retrieve the ratio of oxy- and deoxy-hemoglobin in tissues from data collected by an imaging spectrometer. To retrieve the oxygen saturation fraction from the data, algorithms from the literature using two or three wavelengths were compared to our new algorithm using the many more wavelengths (25 to 60) available in imaging spectrometer data, and noise reduction achieved through principal component transformations. In addition to the analysis of experimental spectral imagery, an oxygen saturation phantom of size 128x128 pixels was simulated. In the forward process, a reflectance image was constructed from an assumed oxygen saturation map and the absorption coefficients of oxy-hemoglobin, deoxy-hemoglobin, melanin and other chromophores. The reflectance data have 60 bands spanning 400 nm to 990 nm with 10 nm intervals in the spectral dimension. Varying amounts of white Gaussian noise was added to the reflectance data to simulate measurement errors in an actual experiment. In the backward process, an oxygen saturation image was reconstructed by applying the algorithm to study the effect of measurement error on the retrieved saturation fraction. The resultant images were evaluated by their mean squared error
