Comparison of endmember extraction techniques

Image pixels represent either distinct materials (end members) that are present in the image, or mistures of two or more of these pure materials. Estimates of pure end member spectra are needed for spectral libraries and for input into pixel unmixing codes. We investigate three algorithms for estimating end member spectra: (1) the convex hull method in which an n-dimensional surface is shrink- wrapped around the data cloud; (2) a pixel-by-pixel search method in which pixels that have sufficiently different spectral angles are declared end members; (3) a pixel-by- pixel search method using Euclidean distance as a measure, followed by clustering to improve the estimate of the spectra. The convex hull technique should provide an estimate of pure end member spectra while the pixel-by-pixel search methods should find both distinct end members and distinct mixtures. Each method requires user-set thresholds to find distinct spectra, which can be expressed in spectral angle degrees or image-dependent units for Euclidean distance. Estimates for the lower threshold (below which two spectra are considered to be the same material) and the upper threshold (above which two spectra are definitely different materials) are derived empirically. Low-altitude AVIRIS data will be used to demonstrate the utility of these end member extraction methods. We will illusxtrate how well each technique compare to the other, and compare how well individual algorithms work across adjacent scenes.