The design and use of hyperspectral imaging remote sensing systems involve the selection of a large number of parameters in particular due to the richness of the data. Many of these parameters interrelate in their effect on system performance. The tasks of optimizing parameter values that one has control over and understanding the impact of those that are uncontrollable are important in system design and use. A computational model of relevant system components and parameter values for a hyperspectral remote sensing system has been developed and used to explore their relative impact on system performance. The hyperspectral remote sensing system is defined by considering the scene and all contributions to the upwelling radiance, the sensor and all effects leading to measured data, and the subsequent processing applied to extract the desired information which is then used as the metric for system performance. The relative contribution to system performance is studied by defining a nominal configuration for the system and then perturbing individual parameters and examining the impact on system performance that results from these perturbations. By considering the effect of parameter values one at a time, the relative impact can be studied. However, since the entire system is still considered in the analysis, the constraining interrelationships are still present thus providing a more relevant indication of impact. Results will be presented for a canonical scenario where an airborne hyperspectral sensor observes a scene where an unresolved object is arbitrarily located and the performance metric is detection accuracy. Significant effects in system performance are seen to be attributable to spectral channel selection and the object spectral characteristics and size, while factors such as instrument noise and calibration error play relatively minor roles in system performance.
Date of creation, presentation, or exhibit
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Kerekes, John, "Parameter impacts on hyperspectral remote sensing system performance" (1996). Accessed from
RIT – Main Campus