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Proceedings Paper

Deep neural networks for compressive hyperspectral imaging
Author(s): Dennis J. Lee
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Paper Abstract

We investigate deep neural networks to reconstruct and classify hyperspectral images from compressive sensing measurements. Hyperspectral sensors provide detailed spectral information to differentiate materials. However, traditional imagers require scanning to acquire spatial and spectral information, which increases collection time. Compressive sensing is a technique to encode signals into fewer measurements. It can speed acquisition time, but the reconstruction can be computationally intensive. First we describe multilayer perceptrons to reconstruct compressive hyperspectral images. Then we compare two different inputs to machine learning classifiers: compressive sensing measurements and the reconstructed hyperspectral image. The classifiers include support vector machines, K nearest neighbors, and three neural networks (3D convolutional neural networks and recurrent neural networks). The results show that deep neural networks can speed up the time for the acquisition, reconstruction, and classification of compressive hyperspectral images.

Paper Details

Date Published: 6 September 2019
PDF: 19 pages
Proc. SPIE 11130, Imaging Spectrometry XXIII: Applications, Sensors, and Processing, 1113006 (6 September 2019); doi: 10.1117/12.2528048
Show Author Affiliations
Dennis J. Lee, Sandia National Labs. (United States)

Published in SPIE Proceedings Vol. 11130:
Imaging Spectrometry XXIII: Applications, Sensors, and Processing
Emmett J. Ientilucci, Editor(s)

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