Compressive hyperspectral imaging by random separable projections in both spatial and spectral domains
نویسندگان
چکیده
An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with compressive sensing of large data, which is typical to hyperspectral imaging. The system allows to optimize the ratio between the spatial and the spectral compression sensing ratio. OCIS codes: 110.4155, 110.4190, 110.4234, 110.1758
منابع مشابه
Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains.
An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectra...
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