ARCHIVES

1. J. Smith, A. Kumar, and L. Zhang, “Deep learning for SAR target classification: A review,” IEEE Transactions on Geoscience and Remote
Sensing, vol. 60, pp. 1234–1248, 2024.
2. M. Chen, P. Li, and S. Wang, “Real-time SAR vehicle detection using YOLO-based networks,” in Proc. International Conference on Radar
Systems, 2023, pp. 112–119.
3. R. Patel and J. Lee, “Generative adversarial networks for SAR image augmentation,” IEEE Access, vol. 9, pp. 56789–56799, 2021.
4. Y. Zhao, H. Liu, and F. Sun, “Multi-feature fusion for robust target recognition in SAR imagery,” Remote Sensing Letters, vol. 14, no. 2,
pp. 198–205, 2023.
5. G. Kumar and T. Das, “Noise suppression and feature extraction in SAR images using deep CNNs,” Journal of Applied Remote Sensing,vol. 15, no. 1, p. 016502, 2024.
6. S. Nguyen and K. Tran, “Multi-canonical correlation analysis for feature fusion in SAR target classification,” IEEE Signal Processing Letters,
vol. 28, pp. 276–280, 2021.
7. D. Kim, J. Park, and S. Choi, “Hybrid SAR target recognition with CNN and SVM,” in Proc. International Conference on Image Processing,
2022, pp. 549–553.
8. L. Wang and M. Zhao, “Enhancing SAR target classification with synthetic data and multi-modal learning,” IEEE Journal of Selected Topics
in Applied Earth Observations and Remote Sensing, vol. 16, pp. 2234–2243, 2023.
9. A. Gupta and N. Singh, “Comprehensive deep learning framework for SAR military target recognition,” Defense Technology, vol. 19, no.
7, pp. 2045–2054, 2024.
10. J. Park and H. Kim, “Real-time object detection in SAR images using YOLOv5,” Journal of Defense Modeling and Simulation, vol. 21, no.
3, pp. 189–200, 2023.
11. S. Lee, Y. Cho, and K. Han, “Speckle noise reduction in SAR imagery with deep residual networks,” Remote Sensing, vol. 15, no. 5, p.
1203, 2023.
12. V. Murphy and D. Patel, “Support vector machine based taxonomy for SAR target recognition,” in Proc. IEEE International Geoscience and
Remote Sensing Symposium, 2022, pp. 794–798.