Dimension Reduction by Random Projection for Endmember Extraction

Random Projection (RP) has been proven to be a powerful technique for Dimension Reduction (DR). In this paper, it is applied to hyperspectral images as a DR preprocess step for Endmember Extraction (EE). Theoretical analysis demonstrates that RP can preserve geometric simplex fitting by hyperspectral data perfectly. Therefore, endmembers, which play an extremely important role for Spectral Mixture Analysis (SMA) of hyperspectral images, can be extracted from the projected data in a subspace by RP and the computational complexity of EE can be greatly reduced. Experimental results demonstrate that RP is computational efficient and data-independent DR technique for EE.