Compressive and classical hyperspectral systems: a fundamental comparison

Adi Shay; Isaac Y. August; Adrian Stern

doi:10.1117/12.2177587

14 May 2015 Compressive and classical hyperspectral systems: a fundamental comparison

Adi Shay, Isaac Y. August, Adrian Stern

Proceedings Volume 9484, Compressive Sensing IV; 948408 (2015) https://doi.org/10.1117/12.2177587
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Hyperspectral imagery involves capturing and processing a tremendous amount of data, which sets severe system resource requirements. This has motivated the application of compressive sensing for different spectroscopic and spectroscopic imager systems. Several new compressive hyperspectral architectures have been designed to stretch the common limitations of classical systems. However, the application of the compressive sensing framework involves design of system architectures that differ significantly from the conventional ones. Since compressive sensing differs essentially from conventional sensing, it cannot be implemented for hyperspectral imaging by simply modifying one of the components of a conventional hyperspectral system, rather it requires a complete new design. In this work we present a comparison between four compressive hyperspectral architectures to conventional architectures. The compressive hyperspectral sensing compared are: Coded Aperture Snapshot Spectral Imaging (CASSI), Compressive HS Imaging by Separable Spatial And Spectral Operators (CHISSS), (Liquid-crystal Compressive spectral Imager) LiCSI and (Spectral Single-Pixel) SSP systems. Those methods are compared to conventional spatial/spectral scanning hyperspectral such as pushbroom, whiskbroom and color filter techniques. A fundamental comparison between these architectures is presented in terms of optical system volume and radiometric efficiency.

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Adi Shay, Isaac Y. August, and Adrian Stern "Compressive and classical hyperspectral systems: a fundamental comparison", Proc. SPIE 9484, Compressive Sensing IV, 948408 (14 May 2015); https://doi.org/10.1117/12.2177587

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KEYWORDS

Imaging systems

Hyperspectral imaging

Image compression

Remote sensing

Compressed sensing

Imaging spectroscopy

Optical components

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