General spectral utility metric for spectral imagery

Abstract

Published approaches to assessing and predicting spectral image utility are generally based on regression methods which fit coefficients to an equation with terms representing spatial scale, spectral fidelity, and signal-to-noise. Such approaches are patterned after the National Imagery Interpretability Rating Scale General Image Quality Equation (NIIRS GIQE) designed for use with remotely-sensed panchromatic imagery. Preliminary testing of these approaches suggests that they will work for some subsets of spectral imagery applications but are not generally applicable to all spectral imaging problems. We present here an approach that gets at the heart of the general problem&minus;assessing the confidence of an image analyst in performing a specified task with a specific spectral image. While applicable in other areas such as health imaging, our approach to spectral utility assessment is presented in this paper from a remote sensing point of view. Our approach allows trade-offs in tasking and system design across the “spectrum” of imagers including panchromatic, multispectral, hyperspectral, and even ultraspectral. Our approach is based on a fusion concept called “semantic transformation.” We assume that spectral and spatial information are largely separable with both contributing to the overall utility of the image. The “semantic transformation” combines the spatial and spectral information in a common term (in our case <i>confidence</i>) to give an overall confidence in performing the specified task. Addressing the spatial and spectral information separately allows us the freedom to assess the information contained in each in ways that the information is actually assimilated (i.e., usually spatial information in exploited visually while spectral information consisting of more than three or four bands is usually exploited by computer algorithms). For the spectral information, we can use either generic exploitation algorithms or the specific algorithms that the image analyst would be expected to use. Testing of our approach was done with a parametric set of simulated imagery where Ground Sampled Distance (GSD) and the number of spectral bands were varied. Our initial test led to some refinements of our approach, which are discussed.