Recursive orthogonal vector projection algorithm for linear spectral unmixing

摘要

Orthogonal vector projection (OVP) is recently developed as a versatile technique which can used in various application in hyperspectral imaging such as subpixel detection, linear spectral unmixing and endmember finding. A great advantage of OVP is that only calculations of vector products are required with no need of matrix multiplications and inverse calculations. Furthermore, this paper develops a recursive version of OVP, to be called recursive OVP (ROVP) so that OVP can be performed vector by vector recursively without using previously processed vectors. As a result, the computational complexity of ROVP is much lower than other algorithms. Furthermore, the ROVP is much easier to be applied to hardware such as FPGA or GPU in the future.