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- W2765431141 abstract "Compressive sensing (CS) takes advantage of the spatial and spectral redundancy in hyperspectral imagery to take fewer measurements than traditional sensors. We simulate compressively sensed hyperspectral airborne images of a HyMap image of Cooke City, Montana using the Coded Aperture Snapshot Spectral Imager Dual Disperser (CASSI-DD) sensor model. Flake et al's novel algorithm (2013), which incorporates both spatial total variation (TV) and spectral smoothing, is used to reconstruct the hyperspectral image cube from the CS measurement.[1] We evaluate the effect of the number of physical measurements (nt) on atmospheric compensation, anomaly detection and target detection. The results indicate that the utility of CS is application-dependent and the optimal nt value is application-driven." @default.
- W2765431141 created "2017-11-10" @default.
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- W2765431141 date "2013-06-01" @default.
- W2765431141 modified "2023-10-16" @default.
- W2765431141 title "Analysis and utility of atmospheric compensation of simulated compressive sensing (CS) measurements" @default.
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- W2765431141 doi "https://doi.org/10.1109/whispers.2013.8080746" @default.
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