Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

On the Value of CTIS imagery for Neural Network Based Classification Experimental Results

Not Accessible

Your library or personal account may give you access

Abstract

The computed tomography imaging spectrometer (CTIS) is a snapshot hyper-spectral imaging system which has recently been demonstrated of value when used in a compressed learning mode. In such a mode, the raw data are not reconstructed in an hyperspectral cube but are directly transmitted to a neural network to perform classification. While the previous investigations on this topic were limited to a simulation perspective, we extend these results to real images and demonstrate the possibility to train the network on simulated data and apply this trained model successfully on real images.

© 2021 The Author(s)

PDF Article  |   Presentation Video
More Like This
Classification of Turbulence-Degraded Imagery Using Neural Networks

Daniel A. LeMaster, Steven Leung, and Olga L. Mendoza-Schrock
PTu4C.1 Propagation Through and Characterization of Atmospheric and Oceanic Phenomena (pcAOP) 2021

Single-Pixel Image Classification via Nonlinear Optics and Deep Neural Network

Santosh Kumar, Ting Bu, He Zhang, Irwin Huang, and Yu-Ping Huang
AW3E.3 CLEO: QELS_Fundamental Science (CLEO:FS) 2021

AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT

Dennis J. Lee, John Mulcahy-Stanislawczyk, Edward Jimenez, Derek West, Ryan Goodner, and Collin Epstein
DF4F.2 Digital Holography and Three-Dimensional Imaging (DH) 2021

Presentation Video

Presentation video access is available to:

  1. Optica Publishing Group subscribers
  2. Technical meeting attendees
  3. Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads


More Like This
Classification of Turbulence-Degraded Imagery Using Neural Networks

Daniel A. LeMaster, Steven Leung, and Olga L. Mendoza-Schrock
PTu4C.1 Propagation Through and Characterization of Atmospheric and Oceanic Phenomena (pcAOP) 2021

Single-Pixel Image Classification via Nonlinear Optics and Deep Neural Network

Santosh Kumar, Ting Bu, He Zhang, Irwin Huang, and Yu-Ping Huang
AW3E.3 CLEO: QELS_Fundamental Science (CLEO:FS) 2021

AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT

Dennis J. Lee, John Mulcahy-Stanislawczyk, Edward Jimenez, Derek West, Ryan Goodner, and Collin Epstein
DF4F.2 Digital Holography and Three-Dimensional Imaging (DH) 2021

Ensemble Learning of Diffractive Optical Neural Networks

Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu, Yair Rivenson, and Aydogan Ozcan
CM6B.2 Computational Optical Sensing and Imaging (COSI) 2021

Misalignment Tolerant Diffractive Optical Networks

Deniz Mengu, Yifan Zhao, Nezih T. Yardimci, Yair Rivenson, Mona Jarrahi, and Aydogan Ozcan
FM3L.7 CLEO: QELS_Fundamental Science (CLEO:FS) 2021

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved