基于多尺度生成对抗网络的SAR图像样本增广 下载: 1168次
李诗怡, 付光远, 崔忠马, 杨小婷, 汪洪桥, 陈雨魁. 基于多尺度生成对抗网络的SAR图像样本增广[J]. 激光与光电子学进展, 2020, 57(20): 201018.
Shiyi Li, Guangyuan Fu, Zhongma Cui, Xiaoting Yang, Hongqiao Wang, Yukui Chen. Data Augmentation in SAR Images Based on Multi-Scale Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201018.
[1] Wang ZW, SheQ, Ward TE. Generative adversarial networks: a survey and taxonomy[EB/OL]. [2020-01-31].https:∥arxiv.org/abs/1701. 04862.
[2] 易维, 曾湧, 原征. 基于NSCT变换的高分三号SAR与光学图像融合[J]. 光学学报, 2018, 38(11): 1110002.
[4] Hammer H, Schulz K. Coherent simulation of SAR images[J]. Proceedings of SPIE, 2009, 7477: 74771G.
[5] 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, 2019: 113- 123.
[6] Goodfellow IJ, Pouget-AbadieJ, MirzaM, et al. Generative adversarial nets[EB/OL]. [2020-01-27].https:∥arxiv.org/abs/1406. 2661.
[7] ArjovskyM, BottouL. Towards principled methods for training generative adversarial networks[EB/OL]. [2020-02-05].https:∥arxiv.org/abs/1701. 04862.
[8] MirzaM, OsinderoS. Conditional generative adversarial nets[EB/OL]. [2020-01-30].https:∥arxiv.org/abs/1411. 1784.
[9] ChenX, DuanY, HouthooftR, et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets[EB/OL]. [2020-02-03].https:∥arxiv.org/abs/1606. 03657.
[10] IsolaP, Zhu JY, Zhou TH, et al. Image-to-image translation with conditional adversarial networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 5967- 5976.
[11] Zhu JY, ParkT, IsolaP, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2242- 2251.
[12] ChoiY, ChoiM, KimM, et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 8789- 8797.
[13] AjocskyM, ChintalaS, BottouL. Wasserstein GAN[EB/OL]. [2020-02-04].https:∥arxiv.org/abs/1701. 07875.
[14] GulrajaniI I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein GAN[EB/OL].[2020-01-27]. https:∥arxiv.org/abs/1704. 00028.
[15] Cui Z Y, Zhang M R, Cao Z J, et al. Image data augmentation for SAR sensor via generative adversarial nets[J]. IEEE Access, 2019, 7: 42255-42268.
[16] Shaham TR, DekelT, MichaeliT. SinGAN: learning a generative model from a single natural image[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 4569- 4579.
[17] SzegedyC, VanhouckeV, IoffeS, et al. Rethinking the inception architecture for computer vision[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 2818- 2826.
[18] Wang XT, YuK, Wu SX, et al. ESRGAN: enhanced super-resolution generative adversarial networks[M] ∥ Leal-Taixé L, Roth S, et al. Computer Vision -ECCV 2018. Lecture Notes in Computer Science. Cham: Springer, 2018, 11133: 63- 79.
[19] HuangG, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 2261- 2269.
[20] LimB, SonS, KimH, et al. Enhanced deep residual networks for single image super-resolution[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 1132- 1140.
[21] SzegedyC, IoffeS, VanhouckeV, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[EB/OL]. [2020-01-30].https:∥arxiv.org/abs/1602. 07261.
[22] 李健伟, 曲长文, 彭书娟, 等. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测[J]. 电子与信息学报, 2019, 41(1): 143-149.
Li J W, Qu C W, Peng S J, et al. Ship detection in SAR images based on generative adversarial network and online hard examples mining[J]. Journal of Electronics & Information Technology, 2019, 41(1): 143-149.
李诗怡, 付光远, 崔忠马, 杨小婷, 汪洪桥, 陈雨魁. 基于多尺度生成对抗网络的SAR图像样本增广[J]. 激光与光电子学进展, 2020, 57(20): 201018. Shiyi Li, Guangyuan Fu, Zhongma Cui, Xiaoting Yang, Hongqiao Wang, Yukui Chen. Data Augmentation in SAR Images Based on Multi-Scale Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201018.