Conventional to Deep Ensemble Methods for Hyperspectral Image Classification: A Comprehensive Survey-Reference-Cited by-同舟云学术

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Conventional to Deep Ensemble Methods for Hyperspectral Image Classification: A Comprehensive Survey

Published:2024 Issue: Volume:17 Page:3878-3916
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:

Ullah Farhan¹^ORCID,Ullah Irfan²^ORCID,Khan Rehan Ullah³^ORCID,Khan Salabat¹^ORCID,Khan Khalil⁴^ORCID,Pau Giovanni⁵^ORCID

Affiliation:

1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

2. School of Computer Science, Chengdu University of Technology, Chengdu, China

3. Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

4. Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan

5. Kore University of Enna, Enna, Italy

Funder

Kore University of Enna and IEEE mutual agreement

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10398446.pdf?arnumber=10398446

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3. SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images;Applied Sciences;2024-08-21

4. Multi-Feature Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification;Remote Sensing;2024-07-29

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