Statistics – Applications
Scientific paper
Aug 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001spie.4381..527c&link_type=abstract
Proc. SPIE Vol. 4381, p. 527-538, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Sylvia S. Shen; Mi
Statistics
Applications
Scientific paper
In this paper the design of a high-throughput imaging spectrometer for use in astronomical applications is proposed and investigated. The method of spectral separation used in this new design does not rely on dispersion through a grating or path length difference in an interferometer. Instead, the chromatic aberration found in a common lens is used to process the input scene through many different spectral point-spread functions, which produce a collection of broadband images. The spectral separation is achieved through a spectral/spatial recovery algorithm. Because there is no grating to block the light or no beam-splitter to reflect it back out of the telescope, a system built on this principle of spectral separation can achieve a throughput in excess of 90%. The spectral diversity in the point-spread functions is achieved by changing the distance between the lenses and detector in the imaging system. The result of these changes is a different chromatic aberration for each wavelength. From a set of measurements taken with different chromatic aberrations, a spectral/spatial recovery algorithm is developed that is capable of extracting spectral information from a set of broadband images that have been processed through different realizations of chromatic aberration. A point design for this spectral separation concept is presented and tested through simulation. The algorithm is tested using simulated scene data from the point design of a small-extended object and a point source.
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