少样本条件下基于生成对抗网络的遥感图像数据增强 下载: 857次
姜雨辰, 朱斌. 少样本条件下基于生成对抗网络的遥感图像数据增强[J]. 激光与光电子学进展, 2021, 58(8): 0810022.
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.
[2] Zhong Z, Zheng L, Kang G L, et al. Random erasing data augmentation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 13001-13008.
[5] 张祥东, 王腾军, 杨耘, 等. 基于多尺度残差网络的小样本高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(16): 162801.
[6] 晋玮佩, 郭继昌, 祁清, 等. 基于条件生成对抗网络的水下图像增强[J]. 激光与光电子学进展, 2020, 57(14): 141002.
[7] 贺琪, 李瑶, 宋巍, 等. 小样本的多模态遥感影像高层特征融合分类[J]. 激光与光电子学进展, 2019, 56(11): 111001.
[8] Cubuk ED, ZophB, ManéD, 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. New York: IEEE Press, 2019: 113- 123.
[14] GoodfellowI, PougetJ, MirzaM, 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, 2014: 2672- 2680.
[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, 2017, 55(5): 2486-2498.
姜雨辰, 朱斌. 少样本条件下基于生成对抗网络的遥感图像数据增强[J]. 激光与光电子学进展, 2021, 58(8): 0810022. 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.