Synthetic Images Augmentation for Robust SAR Target Recognition-Reference-Cited by-同舟云学术

Synthetic Images Augmentation for Robust SAR Target Recognition

Published:2021-12-22 Issue: Volume: Page:
ISSN:
Container-title:2021 The 5th International Conference on Video and Image Processing
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Author:

Lei Yongzhen¹,Xia Weijie¹,Liu Zhe¹

Affiliation:

1. Key Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511176.3511180

Reference28 articles.

1. Sergey Abramov Victoriya Abramova Vladimir Lukin Nikolay Ponomarenko Benoit Vozel Kacem Chehdi Karen Egiazarian and Jaakko Astola. 2014. Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data. (2014). Sergey Abramov Victoriya Abramova Vladimir Lukin Nikolay Ponomarenko Benoit Vozel Kacem Chehdi Karen Egiazarian and Jaakko Astola. 2014. Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data. (2014).

2. Properties of speckle integrated with a finite aperture and logarithmically transformed

3. Ray-Tracing Simulation Techniques for Understanding High-Resolution SAR Images

4. Explainability of Deep SAR ATR Through Feature Analysis

5. Miriam Cha , Arjun Majumdar , H.T. Kung , and Jarred Barber . 2018 . Improving Sar Automatic Target Recognition Using Simulated Images Under Deep Residual Refinements. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2606–2610 . Miriam Cha, Arjun Majumdar, H.T. Kung, and Jarred Barber. 2018. Improving Sar Automatic Target Recognition Using Simulated Images Under Deep Residual Refinements. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2606–2610.

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