Deep learning for compressive hyperspectral sensing

Adrian Stern; Noam Katz; Nadav Cohen; Shauli Shmilovich; Daniel Gedalin; Yaniv Oiknine

doi:10.1117/12.2568596

20 August 2020 Deep learning for compressive hyperspectral sensing

Adrian Stern, Noam Katz, Nadav Cohen, Shauli Shmilovich, Daniel Gedalin, Yaniv Oiknine

Author Affiliations +

Proceedings Volume 11504, Imaging Spectrometry XXIV: Applications, Sensors, and Processing; 115040E (2020) https://doi.org/10.1117/12.2568596
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

The application of compressive sensing (CS) techniques for the hyperspectral (HS) imaging is very appealing since the acquisition of HS images is demanding in terms hardware and acquisition time, and since the application of CS framework matches well the HS imaging task, which involves capturing huge amount of typically very redundant data. During the last decade, we developed several CS HS imaging systems, which have demonstrated orders of magnitude reduction of the acquisition time and of storage requirements, improved signal-to-noise ratio, and reduction of the systems’ size and weight. In this paper we demonstrate how these systems can further benefit from employing deep learning (DL) tools for post-processing of the compressively sensed hyperspectral data. We overview some DL techniques that we have developed for improving the HS image reconstruction and target detection.

Conference Presentation

Citation Download Citation

Adrian Stern, Noam Katz, Nadav Cohen, Shauli Shmilovich, Daniel Gedalin, and Yaniv Oiknine "Deep learning for compressive hyperspectral sensing", Proc. SPIE 11504, Imaging Spectrometry XXIV: Applications, Sensors, and Processing, 115040E (20 August 2020); https://doi.org/10.1117/12.2568596

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