Quantitative assessment of the different methods addressing the endmember variability

Spectral mixture analysis is an important technique to extract desired information from the mixed remotely sensed data. However, current spectral mixture analysis techniques suffered from the endmember variability. Quantitative assessment of SMA techniques with simulated data is critical to understand the influence of endmember variability. For that reason, this study has compared five typical spectral mixture analysis addressing endmember variability issue with simulated data. The comparison result shows that MESMA seems to be the best in unmixing accuracy. However, sensitive to noise and large computation loads also made MESMA less satisfactory, while other methods could supersede MESMA at specific situations.