The quantitative forecasting of spectral imaging system performance is an important capability. The ability to accurately predict the effects on utility of the data due to scene conditions, sensor performance, or even algorithm parameters, can be very important. To this end, an analytical modeling tool has been under development to predict end-to-end spectroradiometric remote sensing system performance, and to understand the relative impact of various system parameters on that performance. Recently, data were collected by NASA’s EO-1 Hyperion space-based hyperspectral imager over an area in Southern California including spatially unresolved buildings of known size. The area of interest was also imaged with previous low-altitude overflights of NASA/JPL’s AVIRIS airborne imaging spectrometer. The AVIRIS data provided an opportunity to investigate the accuracy of unmixing analysis applied to the Hyperion image as well as to serve as a source of input data in model forecasts. This paper describes the results of analysis of the remotely sensed data as well as comparisons to predictions made by the analytical performance prediction model. While the empirical analyses provide point results in terms of the abundance of the buildings per pixel, the model predicts the anticipated variation in the abundance estimates given inherent variability of the building roof material and nearby backgrounds. The model is also exercised to show the impact on the abundance estimates from various remote sensing system parameters including sensor noise, radiometric calibration error, and the number of endmembers assumed in the unmixing algorithm. In the example studied, the natural surface variability and the use of endmembers in the unmixing that were not present in the scene were found to have the most impact on the abundance estimates.
Date of creation, presentation, or exhibit
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Kerekes, John; Glennon, Mary Ann; and Lockwood, Ron, "Unmixing analysis: model prediction compared to observed results" (2003). Accessed from
RIT – Main Campus