Hyperspectral Imaging System Modeling

■ To support hyperspectral sensor system design and parameter trade-off investigations, Lincoln Laboratory has developed an analytical end-to-end model that forecasts remote sensing system performance. The model uses statistical descriptions of scene class reflectances and transforms them to account for the effects of the atmosphere, the sensor, and any processing operations. System-performance metrics can then be calculated on the basis of these transformed statistics. The model divides a remote sensing system into three main components: the scene, the sensor, and the processing algorithms. Scene effects modeled include the solar illumination, atmospheric transmittance, shade effects, adjacency effects, and overcast clouds. Modeled sensor effects include radiometric noise sources, such as shot noise, thermal noise, detector readout noise, quantization noise, and relative calibration error. The processing component includes atmospheric compensation, various linear transformations, and a number of operators used to obtain detection probabilities. Models have been developed for several imaging spectrometers, including the airborne Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument, which covers the reflective solar spectral region from 0.4 to 2.5 μm. This article presents the theory and operation of the model, and provides example parameter trade studies to show the utility of the model for system design and sensor operation applications.