RS-DeepSuperLearner: fusion of CNN ensemble for remote sensing scene classification
Author:
Affiliation:
1. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 2454, Riyadh 11451, Saudi Arabia
Funder
King Saud University
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences,Computer Science Applications
Link
https://www.tandfonline.com/doi/pdf/10.1080/19475683.2023.2165544
Reference120 articles.
1. Multi-Instance Neural Network Architecture for Scene Classification in Remote Sensing
2. An ensemble learning approach for the classification of remote sensing scenes based on covariance pooling of CNN features
3. Alhichri, H. “KSA Remote Sensing Datasets,” Alhichri Research Page. http://alhichri.36bit.com/ksa_dataset.html (accessed Dec. 01, 2020a).
4. Alhichri, H. “Remote Sensing Datasets,” Alhichri Research Page. http://alhichri.36bit.com/research.html (accessed Dec. 01, 2020b).
5. Multitask Classification of Remote Sensing Scenes Using Deep Neural Networks
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