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Proceedings Paper

On the robustness of compressive sensing hyperspectral image reconstruction using convolutional neural network
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Paper Abstract

Hyperspectral imaging is applied in a wide range of defense, security and law enforcement applications. The spectral data caries valuable information for tasks such as identification, detection, and classification. However, the capturing of the spectral information, together with the spatial information, requires a significant acquisition effort. In the recent years we have developed several compressive hyperspectral imaging techniques demonstrating reduction of the captured data by at least an order of magnitude. However, compressive sensing techniques typically require computational heavy and time consuming iterative reconstruction algorithms. The computational burden is even more prominent in compressive spectral imaging due to the large amount of data involved. In this work we demonstrate the utilization of a convolutional neural network (CNN) for the reconstruction of spectral images captured with our Compressive Sensing -Miniature Ultraspectral Imager (CS-MUSI). We discuss the challenges of training the CNN for CS-MUSI and analyze the CNNbased reconstruction performance.

Paper Details

Date Published: 19 September 2019
PDF: 6 pages
Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690L (19 September 2019); doi: 10.1117/12.2533113
Show Author Affiliations
Daniel Gedalin, Ben-Gurion Univ. of the Negev (Israel)
Yaron Heiser, Ben-Gurion Univ. of the Negev (Israel)
Yaniv Oiknine, Ben-Gurion Univ. of the Negev (Israel)
Adrian Stern, Ben-Gurion Univ. of the Negev (Israel)

Published in SPIE Proceedings Vol. 11169:
Artificial Intelligence and Machine Learning in Defense Applications
Judith Dijk, Editor(s)

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