Hyperspectral spectrometers can record electromagnetic energy with hundreds or thousands of spectral channels. With such high spectral resolution, the spectral information has better capability for material identification. Because of the spatial resolution, one pixel in hyperspectral images usually covers several meters, and it may contain more than one material. Therefore, the mixture model must be considered. Linear mixture model (LMM) has been widely used for remote sensing target classifications, because of its simplicity and yields reasonable results for smooth surfaces. For rough surfaces, the physical interactions of the light scattered between multiple materials in the scene must be considered. Recently, Generalized Bilinear Model (GBM) is proposed and it includes the double reflection between different materials into a nonlinear model, but it ignores the interactions within the same material. In this study, we propose a modified version of GBM to further consider this effect in our model, called Modified Generalized Bilinear Model (MGBM).
Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model
