Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

Daniel Gedalin; Yaniv Oiknine; Isaac August; Dan G. Blumberg; Stanley R. Rotman; Adrian Stern

doi:10.1117/1.OE.56.4.041312

9 January 2017 Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

Daniel Gedalin, Yaniv Oiknine, Isaac August, Dan G. Blumberg, Stanley R. Rotman, Adrian Stern

Author Affiliations +

Optical Engineering, Vol. 56, Issue 4, 041312 (January 2017). https://doi.org/10.1117/1.OE.56.4.041312

Abstract

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

Citation Download Citation

Daniel Gedalin, Yaniv Oiknine, Isaac August, Dan G. Blumberg, Stanley R. Rotman, and Adrian Stern "Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system," Optical Engineering 56(4), 041312 (9 January 2017). https://doi.org/10.1117/1.OE.56.4.041312

Received: 11 October 2016; Accepted: 19 December 2016; Published: 9 January 2017

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