Evaluating column subset selection methods for endmember extraction in hyperspectral unmixing

This paper explores the use of column subset selection (CSS) methods for endmember extraction in hyperspectral unmixing. CSS algorithms look for the subset of columns in a matrix that result in the simplex of maximum volume which is similar to the objective in many endmember extraction algorithms such as N-FINDR. Therefore, it is of interest to explore the use of CSS algorithms to solve the endmember extraction problem in hyperspectral unmixing. Many deterministic and randomized algorithms have been proposed in the literature for CSS. In this paper, we present an experimental comparison between CSS algorithms and traditional geometric-based endmember extraction algorithms such as N-FINDR, PPI and VCA. Experiments are conducted using the HYDICE Urban image. Volume of the resulting simplex and classification accuracies of maps extracted from the estimated abundances are used to evaluate the quality of the extracted endmembers and unmixing results. The SVD-based CSS algorithm (SVDSS) has the overall best performance in both metrics.