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18 May 2013 Evaluation of the CASSI-DD hyperspectral compressive sensing imaging system
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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.
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Maria Busuioceanu, David W. Messinger, John B. Greer, and J. Christopher Flake "Evaluation of the CASSI-DD hyperspectral compressive sensing imaging system", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431V (18 May 2013);

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