Abstract

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.

Publication Date

2003

Comments

Published in MIT's Lincoln Laboratory Journal, Special Issue on Spectral Imaging. The authors gratefully acknowledge the Office of the Deputy Undersecretary of Defense (Science and Technology) and the Spectral Information Technology Applications Center for supporting this work. The authors would also like to thank their colleagues in the Sensor Technology and System Applications group—Kris Farrar and Seth Orloff—for their contributions to the development of the model. Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Article

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Campus

RIT – Main Campus

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