Integrating multiple nonlinear estimators into hyperspectral unmixing
Linear spectral unmixing has been widely used for hyperspectral data interpretation. However, there is a need for nonlinear unmixing methods that can model more complex geometries without the need to resort to prior knowledge about the objects in the scene. In this paper, we present a novel strategy for nonlinear spectral unmixing which combines polytope decomposition (POD) with artificial neural network (ANN)-based learning. Even if no ground-truth information is available, the ANN can still efficiently estimate the order of the nonlinearity involved in the problem and enhance the capacity of the POD method to deliver unmixing performance for a wider range of nonlinearities. The proposed method has been evaluated using both simulated and real scenes, providing promising results.