Performance analyses for multispectral imaging systems

David Braun; Vladimir Alperovich; Michael J. Berger

doi:10.1117/12.392537

24 July 2000 Performance analyses for multispectral imaging systems

David Braun, Vladimir Alperovich, Michael J. Berger

Proceedings Volume 4029, Targets and Backgrounds VI: Characterization, Visualization, and the Detection Process; (2000) https://doi.org/10.1117/12.392537
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Multi-spectral imaging systems are required for global monitoring of land and ocean. In order to design a new multi-spectral spaceborne system developed in Elop, and optimize physical parameters, theoretical analyses were performed. The system consists of twelve narrow spectral bands in the visible spectrum. Each spectral band was selected according to the information required for agriculture and water monitoring. NE(delta) (rho) is the principal driver for system design. NE(delta) (rho) refers to the change in target spectral reflectance, which produces a signal in the sensor equal to the noise level in that sensor. This paper describes the NE(delta) (rho) sensitivity to different kinds of scenarios such as vegetation, water and soils. Sensitivity to spectral bands in the 390-965nm spectrum, sun elevation angles and different atmospheric conditions are also presented. The system performance calculations are based on a new simulation tool developed in-house and on the Modtran code (by ALF - USA) for radiance calculations. Along with Ne(delta) (rho) , other performance parameters are presented such as Signal to Noise Ratio and NE(delta) L. From the analyses presented in this paper, it can be shown that the system design of multi-spectral imager has to take into account both scenario and physical parameters. The performance of the Multi-spectral imager is strongly dependent on the scenario and the atmospheric conditions during photography.

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David Braun, Vladimir Alperovich, and Michael J. Berger "Performance analyses for multispectral imaging systems", Proc. SPIE 4029, Targets and Backgrounds VI: Characterization, Visualization, and the Detection Process, (24 July 2000); https://doi.org/10.1117/12.392537

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KEYWORDS

Signal to noise ratio

Sensors

Sun

Imaging systems

Interference (communication)

Vegetation

Atmospheric sensing

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