Nonlinear mixture model for hyperspectral unmixing

This paper addresses the problem of unmixing hyperspectral images, when the light suffers multiple interactions among distinct endmembers. In these scenarios, linear unmixing has poor accuracy since the multiple light scattering effects are not accounted for by the linear mixture model. Herein, a nonlinear scenario composed by a single layer of vegetation above the soil is considered. For this class of scene, the adopted mixing model, takes into account the second-order scattering interactions. Higher order interactions are assumed negligible. A semi-supervised unmixing method is proposed and evaluated with simulated and real hyperspectral data sets.

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