Accurate reconstruction of hyperspectral images from compressive sensing measurements

John B. Greer; J. Christopher Flake

doi:10.1117/12.2015148

31 May 2013 Accurate reconstruction of hyperspectral images from compressive sensing measurements

John B. Greer, J. Christopher Flake

Proceedings Volume 8717, Compressive Sensing II; 87170E (2013) https://doi.org/10.1117/12.2015148
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

Citation Download Citation

John B. Greer and J. Christopher Flake "Accurate reconstruction of hyperspectral images from compressive sensing measurements", Proc. SPIE 8717, Compressive Sensing II, 87170E (31 May 2013); https://doi.org/10.1117/12.2015148

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