Short wavelength terrestrial backgrounds clutter experiment

David G. Kupperman; Patricia K. Murphy; Robert D. Sears

doi:10.1117/12.186614

14 September 1994 Short wavelength terrestrial backgrounds clutter experiment

David G. Kupperman, Patricia K. Murphy, Robert D. Sears

Proceedings Volume 2282, Ultraviolet Technology V; (1994) https://doi.org/10.1117/12.186614
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Short wavelength terrestrial backgrounds (STB) clutter experiments will be performed by the Midcourse Space Experiment (MSX) satellite using the ultraviolet and visible imagers and spectrographic imagers (UVISI) system. These experiments will obtain both temporal and spatial atmospheric statistics in nadir-looking geometries. A comprehensive database will be acquired and used to quantify variations in the background clutter due to changing viewing geometries and dynamic atmospheric conditions. This database will be used to validate radiance and clutter prediction models such as those used in systems design studies. A systematic pipeline approach has been designed to process and analyze the data. Images will be sorted into fixed passband data and spectrographic images. The full-frame images and sub- frame images will be described by a set of structure model parameters which are in the same format as the NSS (non-stationary stochastic structure) model utilized to model atmospheric IR structure. The statistical parameters will constitute the reduced data products. The approach is dictated by the large volume of data anticipated (> 2 Terabytes) and will report the UV clutter data in the same format as the IR-band clutter measured by the Spirit-III sensor.

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David G. Kupperman, Patricia K. Murphy, and Robert D. Sears "Short wavelength terrestrial backgrounds clutter experiment", Proc. SPIE 2282, Ultraviolet Technology V, (14 September 1994); https://doi.org/10.1117/12.186614

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KEYWORDS

Ultraviolet radiation

Imaging systems

Atmospheric modeling

Image processing

Databases

Stochastic processes

Sensors

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