Linear Fusion of Image Sets for Display

Abstract

Many remote-sensing applications produce large sets of images, such as hyperspectral images or time-indexed image sequences. We explore methods to display such image sets by linearly projecting them onto basis functions designed for the red, green, and blue (RGB) primaries of a standard tristimulus display, for the human visual system, and for the signal-to-noise ratio of the dataset, creating a single color image. Projecting the data onto three basis functions reduces the information but allows each datapoint to be rendered by a single color. Principal components analysis is perhaps the most commonly used linear projection method, but it is data adaptive and, thus, yields inconsistent visualizations that may be difficult to interpret. Instead, we focus on designing fixed basis functions based on optimizing criteria in the perceptual colorspace CIELab and the standardized device colorspace sRGB. This approach yields visualizations with rich meaning that users can readily extract. Example visualizations are shown for passive radar video and Airborne Visible/Infrared Imaging Spectrometer hyperspectral imagery. Additionally, we show how probabilistic classification information can be layered on top of the visualization to create a customized nonlinear representation of an image set.