On the performance of random-projection-based dimensionality reduction for endmember extraction

In this paper, we investigate the use of random-projection-based dimensionality reduction for hyperspectral endmember extraction. It is data-independent and computationally more efficient than other widely used dimensionality reduction methods, such as principal component analysis and maximum noise fraction transform. Based on the preliminary result, random-projection-based dimensionality reduction is capable of providing better endmembers after effective decision fusion.