Stochastic Higher-Order Independent Component Analysis for Hyperspectral Dimensionality Reduction-Reference-Cited by-同舟云学术

Stochastic Higher-Order Independent Component Analysis for Hyperspectral Dimensionality Reduction

Published:2022 Issue: Volume:8 Page:1184-1194
ISSN:2333-9403
Container-title:IEEE Transactions on Computational Imaging
language:
Short-container-title:IEEE Trans. Comput. Imaging

Author:

Lupu Daniela¹,Necoara Ion¹^ORCID,Garrett Joseph L.²,Johansen Tor Arne²^ORCID

Affiliation:

1. Department of Automatic Control and Systems Engineering, University Politehnica Bucharest, Bucharest, Romania

2. Center for Autonomous Marine Operations and Systems, Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Mathematics,Computer Science Applications,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6745852/9679468/09992059.pdf?arnumber=9992059

Reference38 articles.

1. A review of different dimensionality reduction methods for the prediction of sugar content from hyperspectral images of wine grape berries

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3. Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

4. Database of hyperspectral images,0

5. Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising

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