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Optimized 3D-2D CNN for automatic mineral classification in hyperspectral images Cover

Optimized 3D-2D CNN for automatic mineral classification in hyperspectral images

Reports on Geodesy and Geoinformatics
Volume 118 (2024): Issue 1 (December 2024)
By: Youcef Attallah,  Ehlem Zigh and  Ali Pacha Adda  
Open Access
|Nov 2024

Authors

Youcef Attallah

youcef.attallah@univ-usto.dz

Coding and Security of Information Laboratory (LACOSI), Department of Electronics, Faculty of Electrical Engineering, University of Science and Technology of OranMohamed-Boudiaf (USTOMB), Oran, Algeria

Ehlem Zigh

Coding and Security of Information Laboratory (LACOSI), Department of Electronics, Faculty of Electrical Engineering, University of Science and Technology of OranMohamed-Boudiaf (USTOMB), Oran, Algeria

Ali Pacha Adda

Coding and Security of Information Laboratory (LACOSI), Department of Electronics, Faculty of Electrical Engineering, University of Science and Technology of OranMohamed-Boudiaf (USTOMB), Oran, Algeria
DOI: https://doi.org/10.2478/rgg-2024-0017 | Journal eISSN: 2391-8152 | Journal ISSN: 0867-3179
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Language: English
Submitted on: Apr 19, 2024
Accepted on: Sep 3, 2024
Published on: Nov 7, 2024
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
hyperspectral imaging,
mineral identification,
Optimized 3D-2D CNN,
diagnostic absorption band,
feature extraction
Related subjects:
Computer sciences,
Computer sciences, other,
Geosciences,
Geodesy,
Cartography and photogrammetry,
Geosciences, other

© 2024 Youcef Attallah, Ehlem Zigh, Ali Pacha Adda, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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