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Proceedings Paper

Evaluation of the CASSI-DD hyperspectral compressive sensing imaging system
Author(s): Maria Busuioceanu; David W. Messinger; John B. Greer; J. Christopher Flake
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Paper Abstract

Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spatial and spectral dimensions. We utilize a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) CS model to simulate CS measurements from HyMap images. Flake et al's novel reconstruction algorithm, which combines a spectral smoothing parameter and spatial total variation (TV), is used to create high resolution hyperspectral imagery.1 We examine the e ect of the number of measurements, which corresponds to the percentage of physical data sampled, on the delity of simulated data. The impacts of the CS sensor model and reconstruction of the data cloud and the utility for various hyperspectral applications are described to identify the strengths and limitations of CS.

Paper Details

Date Published: 18 May 2013
PDF: 14 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431V (18 May 2013); doi: 10.1117/12.2015445
Show Author Affiliations
Maria Busuioceanu, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
John B. Greer, National Geospatial-Intelligence Agency (United States)
J. Christopher Flake, National Geospatial-Intelligence Agency (United States)


Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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