Nonlinear unmixing of hyperspectral images using a generalized bilinear model

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Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms (2013, methodology): “linear model (GBM) has been proposed in [22] by setting βi,j,p = γi,j,pai,paj,p”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “The BMM and GBM have inspired several posterior methodologies: some use polynomial functions to model the nonlinearities provided by layer partitions and scattering properties in multilayered scenarios [169], while others use different approaches to solve the proposed model [170]– [173].”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “Several strategies have successfully applied the bilinear model to treat the double scattering problem, such as Bayesian algorithms, where prior models are chosen to satisfy the positivity and sum-to-one constraints [60].”
A Review of Nonlinear Hyperspectral Unmixing Methods (2014, background): “Another term often used for bilinear interaction is secondary illumination, indicating that an object is not illuminated by the light source, but by light reflected by another object.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “Conversely, bilinear models have been successively proposed in [58]–[62] to handle scattering effects, e.”