Linear unmixing performance forecasting

The quantitative forecasting of hyperspectral system performance is an important capability at every stage of system development including system requirement definition, system design, and sensor operation. In support of this, Lincoln Laboratory has been developing an analytical modeling tool to predict end-to-end spectroradiometric remote sensing system performance. Recently, the model has been extended to more accurately depict complex natural scenes by including multiple classes in the target pixel through the use of a linear mixing model. Additionally, a linear unmixing algorithm has been implemented to predict retrieved fractional abundances and their associated errors due to both natural variability and corrupting noise sources. This paper describes the details of this multiple target class model enhancement. Comparisons are presented between the model predictions and measured spectral radiances, as well as unmixing results obtained from data collected by NASA’s EO-1 Hyperion space-based hyperspectral sensor. Additionally, results of an analysis using the enhanced model are presented to show the sensitivity of end member fractional abundance estimates to system parameters using linear unmixing techniques.