K-P-Means: A Clustering Algorithm of K "Purified" Means for Hyperspectral Endmember Estimation

This letter presents K-P-Means, a novel approach for hyperspectral endmember estimation. Spectral unmixing is formulated as a clustering problem, with the goal of K-P-Means to obtain a set of “purified” hyperspectral pixels to estimate endmembers. The K-P-Means algorithm alternates iteratively between two main steps (abundance estimation and endmember update) until convergence to yield final endmember estimates. Experiments using both simulated and real hyperspectral images show that the proposed K-P-Means method provides strong endmember and abundance estimation results compared with existing approaches.