Laser & Optoelectronics Progress, Volume. 58, Issue 8, 0810022(2021)

Data Augmentation for Remote Sensing Image Based on Generative Adversarial Networks Under Condition of Few Samples

Yuchen Jiang* and Bin Zhu
Author Affiliations
  • State Key Laboratory of Pulsed Power Laser Technology, College of Electronic Countermeasures, National University of Defense Technology, Hefei, Anhui 230009, China
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    References(25)

    [2] Zhong Z, Zheng L, Kang G L et al. Random erasing data augmentation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 13001-13008(2020).

    [8] Cubuk E D, Zoph B, Mané D et al. AutoAugment: learning augmentation strategies from data[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 113-123(2019).

    [14] Goodfellow I, Pouget J, Mirza M et al. Generative adversarial nets[C]. //Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Quebec, Canada. New York: ACM, 2672-2680(2014).

    [25] Long Y, Gong Y P, Xiao Z F et al. Accurate object localization in remote sensing images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 2486-2498(2017).

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    Yuchen Jiang, Bin Zhu. Data Augmentation for Remote Sensing Image Based on Generative Adversarial Networks Under Condition of Few Samples[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810022

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    Paper Information

    Category: Image Processing

    Received: Jul. 30, 2020

    Accepted: Sep. 22, 2020

    Published Online: Apr. 12, 2021

    The Author Email: Yuchen Jiang (jyc2647118@126.com)

    DOI:10.3788/LOP202158.0810022

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