Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability
L. Drumetz, M. A. Veganzones, S. Henrot, R. Phlypo, J. Chanussot and C. Jutten
IEEE Transactions on Image Processing
Volume: 25 , Issue: 8
DOI : 10.1109/TIP.2016.2579259
Publication Year: 2016 , Page(s): 3890 – 3905
We use the Extended Linear Mixing Model (ELMM) presented in  to unmixing. This model assumes the mixing process is linear, but considers that endmembers are no longer reduced to a single spectral signature, but that they can vary in each pixel of the image. The variations are allowed in the form of scaled versions of reference endmembers, which approximately models spectral variability induced by changing illumination conditions in every pixel. Intrinsic variability of the material can also be captured. In addition, spatial regularizations on the abundances and the scaling factors of the model can be enforced.