Transfer Functions for Imaging Spectroscopy Data using Principal Component Analysis

Abstract

In this paper we present a new application of the principal component analysis (PCA) to generate multidimensional transfer functions. These transfer functions are needed in the volumetric visualization of spectral data to isolate regions that contain interesting peak-shaped features. Both large and small peaks can be equally important and represent the presence of different chemical elements in a dataset. Principal component analysis separates these peaks in different uncorrelated components and can simultaneously identify spatial patterns. This approach is characterized by the direct linkage between the resulting spectral and spatial components. Our method enables us to create an opacity map from these components. One or more mappings can be selected to highlight features in three-dimensional volume visualization.