A generalized kernel for areal and intimate mixtures

Abstract

In previous work, kernel methods were introduced as a way to generalize the linear mixing model for hyperspectral data. This work led to a new physics-based kernel that allowed accurate unmixing of intimate mixtures. Unfortunately, the new physics-based kernel did not perform well on linear mixtures; thus, different kernels had to be used for different mixtures. Ideally, a single unified kernel that can perform both unmixing of areal and intimate mixtures would be desirable. This paper presents such a kernel that can automatically identify the underlying mixture type from the data and perform the correct unmixing method. Results on real-world, ground-truthed intimate and linear mixtures demonstrate the ability of this new data-driven kernel to perform generalized unmixing of hyperspectral data.