Nonlinear mixture analysis for hyperspectral imagery

Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another ¿endmember¿, representing nonlinear scattering effect during pixel construction process. The analysis is followed by original linear demixing process. Due to the larger number of nonlinear terms being added, the resulting abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. We take advantage of the developed endmember variable linear mixture model (EVLMM) to search the actual endmember set for each pixel, which yields more accurate abundance estimation.