Orthogonal matching pursuit for nonlinear unmixing of hyperspectral imagery

Abstract

A simple but effective nonlinear mixture model is adopted for nonlinear unmixing of hyperspectral imagery, where the multiplication of each pair of endmembers results in a virtual endmember, representing multiple scattering effect during pixel construction process. The analysis is followed by linear unmixing for abundance estimation. Due to a large number of nonlinear terms being added in an unknown environment, the following abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. Thus, sparse unmixing is applied to search the actual endmember set per pixel. The orthogonal matching pursuit (OMP) is adopted for this purpose. It can offer comparable results to the previously developed endmember variable linear mixture model (EVLMM) with much lower computational cost.