A microscene approach to the evaluation of hyperspectral system level performance
David W. Allen, Ronald G. Resmini, Christopher Deloye
Assessing the ability of a hyperspectral imaging (HSI) system to detect the presence of a substance or to quantify abundance requires an understanding of the many factors in the end-to-end remote sensing scenario from scene to sensor to data exploitation. While there are methods which attempt to model such an overall scenario, they are necessarily implemented with assumptions and approximations that do not completely capture the true complexity of the actual radiative transfer processes nor do they capture the range of variability that materials display in a natural setting. We propose one alternative to numerical data models that generates hyperspectral image cubes for system trade studies and for algorithm development and testing. This approach makes use of compact hyperspectral imagers that can be used in the laboratory to measure materials in a 'microscene' specific to ones application. This simple, low-cost method can provide proxy data which provide a distribution of points in n-dimensional (n-D) hyperspace that are indistinguishable from an earth remote sensing scene. The key to acceptance of this approach is quantifying the distributions of spectra as points in n-D space so that one can compare the spectral complexity of laboratory generated microscene data to that of an earth remote sensing scene. The spectral complexity of the microscene generated in the lab is thus compared to airborne remotely sensed HSI. We produce and measure a microscene, estimate its data dimensionality, and compare that to similar estimates of dimensionality of airborne HSI data sets. Signal-to-clutter ratios (SCR) of the microscene are also compared to those derived from airborne HSI data. The results suggest the microscene is capable of producing a scene that is as complex, if not more so, than that of a hyperspectral scene collected from an airborne sensor.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
, Resmini, R.
and Deloye, C.
A microscene approach to the evaluation of hyperspectral system level performance, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
, Baltimore, MD, [online], https://doi.org/10.1117/12.2015834
(Accessed December 2, 2023)