A low-computational-complexity algorithm for hyperspectral endmember extraction: Modified vertex component analysis

Abstract

Endmember extraction represents one of the most challenging aspects of hyperspectral image processing. In this letter, a new algorithm for endmember extraction, named modified vertex component analysis (MVCA), is presented. This new technique outperforms the popular vertex component analysis (VCA) by applying a low-complexity orthogonalization method and by utilizing integer instead of floating-point arithmetic when dealing with hyperspectral data. The feasibility of this technique is demonstrated by comparing its performance with VCA on synthetic mixtures as well as on the well-known Cuprite hyperspectral image. MVCA shows promising results in terms of much lower computational complexity, still reproducing similar endmember accuracy than its original counterpart. Moreover, the features of this algorithm combined with state-of-the-art hardware implementations qualify MVCA as a good potential candidate for all those applications in which real time is a must.