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In this paper, we model a pixel as a linear combination of endmembers sampledfrom probability distributions of Gaussian mixture models (GMM). The parametersof the GMM distributions are estimated using spectral libraries. Abundances areestimated based on the distribution parameters. The advantage of this algorithmis that the model size grows very slowly as a function of the library size. Tovalidate this method, we used data collected by the AVIRIS sensor over theSanta Barbara region: two 16 m spatial resolution and two 4 m spatialresolution images. 64 validated regions of interest (ROI) (180 m by 180 m) wereused to assess estimate accuracy. Ground truth was obtained using 1 m imagesleading to the following 6 classes: turfgrass, non-photosynthetic vegetation(NPV), paved, roof, soil, and tree. Spectral libraries were built by manuallyidentifying and extracting pure spectra from both resolution images, resultingin 3,287 spectra at 16 m and 15,426 spectra at 4 m. We then unmixed ROIs ofeach resolution using the following unmixing algorithms: the set-basedalgorithms MESMA and AAM, and the distribution-based algorithms GMM, NCM, andBCM. The original libraries were used for the distribution-based algorithmswhereas set-based methods required a sophisticated reduction method, resultingin reduced libraries of 61 spectra at 16 m and 95 spectra at 4 m. The resultsshow that GMM performs best among the distribution-based methods, producingcomparable accuracy to MESMA, and may be more robust across datasets.

Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability