Spectral unmixing

TLDR

Spectral unmixing of hyperspectral data, a key step in remote sensing evolution, relies on the linear mixing assumption that has allowed algorithms from diverse fields to be adapted, yet its applicability varies with the circumstances that generate mixed pixels. The study questions whether linearity alone adequately models mixing across all applications. Spectral unmixing produces endmember and abundance estimates that are crucial for determining the material composition of mixtures.

Abstract

Spectral unmixing using hyperspectral data represents a significant step in the evolution of remote decompositional analysis that began with multispectral sensing. It is a consequence of collecting data in greater and greater quantities and the desire to extract more detailed information about the material composition of surfaces. Linear mixing is the key assumption that has permitted well-known algorithms to be adapted to the unmixing problem. In fact, the resemblance of the linear mixing model to system models in other areas has permitted a significant legacy of algorithms from a wide range of applications to be adapted to unmixing. However, it is still unclear whether the assumption of linearity is sufficient to model the mixing process in every application of interest. It is clear, however, that the applicability of models and techniques is highly dependent on the variety of circumstances and factors that give rise to mixed pixels. The outputs of spectral unmixing, endmember, and abundance estimates are important for identifying the material composition of mixtures.