A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification

Author:

Ahmad Muhammad¹^ORCID,Ghous Usman¹,Hong Danfeng²^ORCID,Khan Adil Mehmood³,Yao Jing²^ORCID,Wang Shaohua⁴,Chanussot Jocelyn⁵^ORCID

Affiliation:

1. Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot, Pakistan

2. Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

3. Institute of Data Science and Artificial Intelligence, Innopolis University, Innopolis, Russia

4. International Research Center of Big Data for Sustainable Development Goals, Beijing, China

5. GIPSA-Lab, Grenoble INP, CNRS, University of Grenoble Alpes, Grenoble, France

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference72 articles.

1. Deep learning for hyperspectral image classification;ahmad,2021

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2. Integrating Entropy-Based Classification into Hyper Spectral Images for Improved Object Identification;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29