Modeling spectral mixing for geological mixtures: detecting nonlinearly mixed pixels in hyperspectral image of banded hematite quartzite

Modeling spectral mixing for geological mixtures is challenging. The endmembers in these mixtures interact at a microscopic scale resulting in nonlinear mixing. Prior to applying inversion techniques for spectral unmixing, it is essential to identify the nature of nonlinear mixing that would facilitate a faster analysis of hyperspectral images for geological mixtures. This paper attempts pixel-wise nonlinearity detection of a hyperspectral image of a geological mixture (rock sample) collected in a controlled environment in the laboratory. The identified nonlinearly mixed regions were mapped and further validated through their spectral features in the principal component space. The insights obtained in this study would further support identifying the nature of mixing in geological mixtures.