Application of deep generative networks for SAR/ISAR: a review

Author:

Zhang Jiawei,Liu Zhen,Jiang Weidong,Liu Yongxiang,Zhou Xiaolin,Li Xiang

Funder

National Key Research and Development Program of China under Grant

National Natural Science Foundation of China under Grants

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference167 articles.

1. Ai J, Fan G, Mao Y, Jin J, Xing M, Yan H (2021) An improved SRGAN based ambiguity suppression algorithm for SAR ship target contrast enhancement. IEEE Geosci Remote Sens Lett 19:1–5

2. Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. arXiv:1701.04862

3. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning, PMLR, pp 214–223

4. Baier G, Deschemps A, Schmitt M, Yokoya N (2021) Synthesizing optical and SAR imagery from land cover maps and auxiliary raster data. IEEE Trans Geosci Remote Sens 60:1–12

5. Bank D, Koenigstein N, Giryes R (2020) Autoencoders. arXiv:2003.05991

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