A popular algorithm for unmixing hyperspectral data, namely, Winter's N-FINDR algorithm, is frequently used to benchmark other algorithms or as the basis for new algorithms. The interpretations of this algorithm within the literature are not consistent, and some of these differences have significant impact on the convergence of the algorithm. Despite this, the differences in implementation have not been explicitly acknowledged within the literature, which means that many studies are now ambiguous or incomparable. An examination of various implementations of the N-FINDR algorithm highlights that not all interpretations possess the properties asserted by Winter and that interpretations that consider each pixel multiple times generate much larger simplexes. Regardless of which implementation researchers choose to use, if they are explicit in their choice, this would allow for unambiguous comparisons.

Linear unmixing is a blind source separation problem that decomposes a hyperspectral image into the spectra of the material constituents of the scene and the abundance maps of those materials across that scene. A novel method for determining the material spectra from within the scene, AGES, is proposed based on the positional information contained within abundances generated by additivity-constrained inversion. This new approach is compared on both simulated and real data sets to the well established N-FINDR algorithm, comparing favorably in terms of computational complexity with the existing algorithm without significantly sacrificing accuracy. In addition, the algorithm has some desirable properties inherent in such an approach.