A comparison of kernel functions for intimate mixture models

In previous work, kernel methods were introduced as a way to generalize the linear mixing model. This work led to a new set of algorithms that performed the unmixing of hyperspectral imagery in a reproducing kernel Hilbert space. By processing the imagery in this space different types of unmixing could be introduced - including an approximation of intimate mixtures. Whereas previous research focused on developing the mathematical foundation for kernel unmixing, this paper focuses on the selection of the kernel function. Experiments are conducted on real-world hyperspectral data using a linear, a radial-basis function, a polynomial, and a proposed physics-based kernel. Results show which kernels provide the best ability to perform intimate unmixing.