Anderson, John C.Gosink, Luke J.Duchaineau, Mark A.Joy, Ken I.H.-C. Hege, I. Hotz, and T. Munzner2014-02-212014-02-2120091467-8659https://doi.org/10.1111/j.1467-8659.2009.01480.xWe present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high-dimensional, multi-variate data in which data samples are onedimensional scalar functions. Our approach focuses upon the creation of high-dimensional range-space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high-dimensional spectral imagery, and particulate pollution data from air quality simulations.