Mapping intimate mixtures using an adaptive kernel-based technique

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 adaptive kernel unmixing method that both identified and unmixed linearly and intimately mixed pixels. However, the results from this previous research was limited to lab-based data where the endmembers were known a-priori and atmospheric effects were absent. This paper documents the results of the adaptive kernel-based unmixing techniques on real-world hyperspectral data collected over Smith Island, Virginia, USA. The results show that the adaptive kernel unmixing method can readily identify where nonlinear mixtures exist in the image even when perfect knowledge of the endmembers and the reflectance cannot be known.