Fast Implementation of Pixel Purity Index Algorithm

Pixel purity index (PPI) algorithm has been widely used in hyperspectral image analysis for endmember extraction because of its publicity and availability in the Research Systems ENVI software. In this paper, we develop a fast algorithm to implement the PPI, which provides several significant advantages over the PPI. First, it uses a newly developed concept, virtual dimensionality (VD) to estimate the number of endmembers required to be generated by the algorithm. Second, it uses an endmember initialization algorithm (EIA) to generate an appropriate set of initial endmembers that can reduce a significant number of runs required for the PPI. Third, it provides a new iterative rule and a stopping rule to terminate the algorithm, a feature that is not available in the original PPI which is not an iterative algorithm. Most importantly, unlike the PPI which requires a visualization tool to manually select a final set of endmembers, the FPPI is completely automatic and unsupervised. Since the original PPI algorithm has never been fully disclosed in the literature due to its propriety, the step-by-step algorithmic implementation of FPPI presented in this paper is considered to be new and may be very beneficial to users who are interested in this algorithm without soliciting help from particular software.