Generalized linear mixing model accounting for endmember variability
This work addresses more accurate material identification in hyperspectral imaging for remote sensing applications, but it is incremental as it builds on existing ELMM methodology.
The authors tackled the problem of endmember variability in hyperspectral image unmixing by generalizing the extended linear mixing model (ELMM) to a new GLMM that accounts for complex spectral distortions, and simulations with real and synthetic data showed improved unmixing performance.
Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from illumination changes. In this paper, we further generalize the ELMM leading to a new model (GLMM) to account for more complex spectral distortions where different wavelength intervals can be affected unevenly. We also extend the existing methodology to jointly estimate the variability and the abundances for the GLMM. Simulations with real and synthetic data show that the unmixing process can benefit from the extra flexibility introduced by the GLMM.