End-to-end performance modeling of passive remote sensing systems

Barham W. Smith; Christoph C. Borel; William B. Clodius; James P. Theiler; Bryan E. Laubscher; Paul G. Weber

doi:10.1117/12.241972

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10 June 1996 End-to-end performance modeling of passive remote sensing systems

Barham W. Smith, Christoph C. Borel, William B. Clodius, James P. Theiler, Bryan E. Laubscher, Paul G. Weber

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Proceedings Volume 2743, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VII; (1996) https://doi.org/10.1117/12.241972
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

The ultimate goal of end-to-end system modeling is to simulate all known physical effects which determine the content of the data, before flying an instrument system. In practice we approach this ideal but do not attain it. In remote sensing, one begins with a scene, viewed either statically or dynamically, computes the radiance in each spectral band, renders the scene, transfers it through representative atmospheres to create the radiance field at an aperture, and integrates over sensor pixels. We have simulated a comprehensive sequence of realistic instrument hardware elements and the transfer of simulated data to an analysis system. This analysis package is the same as that intended for use on data collections from the real system. By comparing the analyzed image to the original scene, the net effect of nonideal system components can be understood. Iteration yields the optimum values of system parameters to achieve performance targets. We have used simulation to develop and test improved multispectral algorithms for : (1) the robust retrieval of water surface temperature, water vapor column,and other quantities; (2) the preservation of radiometric accuracy during atmospheric correction and pixel registration on the ground; and (3) exploitation of on- board multispectral measurements to assess the atmosphere between ground and aperture. We have evaluated the errors in these retrievals for a variety of target types due to: telescope OTF, calibration bias, system noise, spacecraft motion and jitter, atmospheric effects, telescope distortions, and co- registration during processing of multispectral images with offset pixels.

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Barham W. Smith, Christoph C. Borel, William B. Clodius, James P. Theiler, Bryan E. Laubscher, and Paul G. Weber "End-to-end performance modeling of passive remote sensing systems", Proc. SPIE 2743, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VII, (10 June 1996); https://doi.org/10.1117/12.241972

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