Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution-Reference-Cited by-同舟云学术

Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution

Published:2024 Issue: Volume:17 Page:1470-1484
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Ulrichsen Alexander¹^ORCID,Murray Paul¹^ORCID,Marshall Stephen¹^ORCID,Gabbouj Moncef²^ORCID,Kiranyaz Serkan³^ORCID,Yamaç Mehmet²^ORCID,Aburaed Nour¹^ORCID

Affiliation:

1. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.

2. Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland

3. Electrical Engineering Department, College of Engineering, Qatar University, Doha, Qatar

Funder

Engineering and Physical Sciences Research Council

Peacock Technology Limited, Business Finland and USA NSF CBL Amalia project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10319126.pdf?arnumber=10319126

Reference57 articles.

1. Hyperspectral remote sensing,2022

2. Limitations of hyperspectral earth observation on small satellites;Villafranca;J. Small Satell.,2012

3. Optical Imaging and Spectroscopy

4. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

5. Semi-supervised student-teacher learning for single image super-resolution

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