Stepwise Simplex Projection Method for Selection of Endmembers in Hyperspectral Images

摘要

In previous research, we introduced a family of simplex projection methods for selection of endmembers in hyperspectral images. In this paper, we define a new member of that family, which we call the Stepwise Simplex Projection (SSP) method. This new method adds and eliminates endmembers based on their distances to simplexes defined by previously chosen endmembers. We compare the SSP method to a previously defined simplex projection method (called the Farthest Pixel Selection method) and to some other methods such as the Pixel Purity Index and Maximum Distance methods. To this end, we introduce several summary measures to describe how well a set of endmembers characterizes the image spectra. We also investigate how well the resulting sets of endmembers perform in subpixel target detection. The numerical results are based on AVIRIS hyperspectral imagery. The SSP method proves to be the most consistently well performing among the investigated methods.