Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning
Abstract
1. Introduction
2. Calculation of Clutter Power Spectrums
3. Estimation of an Atmospheric Refractivity Using Deep Learning
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Hyperparameter | Number of Neurons | Functions | |||
---|---|---|---|---|---|
Epochs | 30 | Layer 1 | 128 | Activation | ReLU |
Minibatch size | 128 | Layer 2 | 64 | Last classification | Softmax |
Learning rate | 0.01 | Layer 3 | 32 | ||
Optimizer | Adam | Layer 4 | 8 | loss | Cross-entropy |
Total Number of Data Items | Validation Accuracy | Prediction Accuracy with the Heuksando Data |
---|---|---|
2800 | 94.35% | 98.31% |
5600 | 95.99% | 98.36% |
8400 | 96.63% | 98.53% |
11,200 | 97.43% | 98.43% |
14,000 | 97.77% | 98.84% |
28,000 | 98.20% | 99.06% |
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Jin, T.; Cho, J.; Jang, D.; Choo, H. Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning. Remote Sens. 2024, 16, 674. https://doi.org/10.3390/rs16040674
Jin T, Cho J, Jang D, Choo H. Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning. Remote Sensing. 2024; 16(4):674. https://doi.org/10.3390/rs16040674
Chicago/Turabian StyleJin, Taekyeong, Jeongmin Cho, Doyoung Jang, and Hosung Choo. 2024. "Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning" Remote Sensing 16, no. 4: 674. https://doi.org/10.3390/rs16040674
APA StyleJin, T., Cho, J., Jang, D., & Choo, H. (2024). Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning. Remote Sensing, 16(4), 674. https://doi.org/10.3390/rs16040674