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

Daniel Gedalin, Yaron Heiser, Yaniv Oiknine, Adrian Stern

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

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.

Original languageEnglish GB
DOIs
StatePublished - 1 Jan 2019
EventArtificial Intelligence and Machine Learning in Defense Applications 2019 - Strasbourg, France
Duration: 10 Sep 201912 Sep 2019

Conference

ConferenceArtificial Intelligence and Machine Learning in Defense Applications 2019
Country/TerritoryFrance
CityStrasbourg
Period10/09/1912/09/19

Keywords

  • Compressive Sensing
  • Deep Neural Networks
  • Hyperspectral Reconstruction
  • Inverse problem solving

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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