[1] Q ZOU, L NI, T ZHANG et al. Deep learning based feature sele-ction for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12, 2321-2325(2015).

[2] C SHORTEN, T M KHOSHGOFTAAR. A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60(2019).

[3] Chengchuang LIN, Chun SHAN, Gansen ZHAO et al. Review of image data augmentation in computer vision. Journal of Frontiers of Computer Science and Technology, 15, 583-611(2021).

[4] Dongao MA, Ping TANG, Lijun ZHAO et al. Review of data augmentation for image in deep learning. Journal of Image and Graphics, 26, 487-502(2021).

[5] Z ZHONG, L ZHENG, G KANG et al. Random Erasing Data Augmentation, 13001-13008(2020).

[6] P CHEN, S LIU, H ZHAO et al. GridMask Data Augmentation. https：∥arxiv.org/abs/2001.04086v2

[7] Hao WANG, Ye ZHANG, Honghai SHEN et al. Review of image enhancement algorithms. Chinese Optics, 10, 438-448(2017).

[8] A B JUNG, K WADA, J CRALL et al. Imgaug. https：∥github. com/aleju/imgaug. 2020-09-25

[9] A BUSLAEV, V I IGLOVIKOV, E KHVEDCHENYA et al. Albumentations： Fast and flexible image augmentations. Information, 11, 125(2020).

[10] E HARRIS, A MARCU, M PAINTER et al. FMix： Enhancing mixed sample data augmentation. https：∥arxiv.org/abs/2002.12047v3

[11] S YUN, D HAN, S J OH et al. CutMix： Regularization Strategy to Train Strong Classifiers with Localizable Features, 6023-6032(2019).

[12] D DWIBEDI, I MISRA, HEBERT M. Cut. Paste and Learn： Surprisingly Easy Synthesis for Instance Detection, 1301-1310(2017).

[13] M JADERBERG, K SIMONYAN, A ZISSERMAN et al. Spatial Transformer Networks, 12, 2017-2025(2015).

[14] B LI, F WU, S N LIM et al. On Feature Normalization and Data Augmentation, 12378-12387(2021).

[15] D BERTHELOT, N CARLINI, I J GOODFELLOW et al. MixMatch： A Holistic Approach to Semi-supervised Learning, 14, 5050-5060(2019).

[16] D HAN, Q LIU, W FAN. A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95, 43-56(2018).

[17] C G MA, Y Y GUO, P WU et al. Review of image enhancement based on generative adversarial networks. Netinfo Security, 19, 10-21(2019).

[18] C HAN, K MURAO, S SATOH et al. Learning more with less： GAN-based medical image augmentation. Medical Imaging Technology, 37, 137-142(2019).

[19] M FRID-ADAR, I DIAMANT, E KLANG et al. GAN-based synthetic medical image augmentation for increased CNN performance in Liver Lesion Classification. Neurocomputing, 321, 321-331(2018).

[20] B LIU, S SU, J WEI. The Effect of data augmentation methods on pedestrian object detection. Electronics, 11, 3185(2022).

[21] Y LU, D CHEN, E OLANIYI et al. Generative Adversarial Networks （GANs） for image augmentation in agriculture： A systematic review. Computers and Electronics in Agriculture, 200(2022).

[22] M D LUO, J CAO, X MA et al. FA-GAN：Face aug-mentation GAN for deformation-invariant face recognition. IEEE Transactions on Information Forensics and Security, 16, 2341-2355(2021).

[23] E D CUBUK, B ZOPH, D MANE et al. AutoAugment： Learning augmentation strategies from data, 113-123(2019).

[24] I KIM, T KIM et al. Fast autoaugment. Neural Information Processing Systems(2019).

[25] E LIANG, I STOICA et al. Population based augmentation： Efficient learning of augmentation policy schedules. ArXiv(2019).

[26] D MA, P TANG, L ZHAO. SiftingGAN： Generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro. IEEE Geoscience and Remote Sensing Letters, 16, 1046-1050(2019).

[27] P ISOLA, J Y ZHU et al. Image-to-image Translation with Conditional Adversarial Networks, 5967-5976(2017).

[28] I GOODFELLOW, J POUGET-ABADIE, M MIRZA et al. Generative adversarial networks. Communications of the ACM, 63, 139-144(2020).

[29] Hong’an LI, Qiaoxue ZHENG, Jing ZHANG et al. Pix2Pix-based grayscale image coloring method. Journal of Computer-Aided Design & Computer Graphics, 33, 929-938(2021).

[30] Y GAO, J M LI, J Z XU et al. Terrain surface texture generation networks for user semantics customization. Journal of System Simulation, 35, 2077-2086(2023).

[31] J E PARK, G KIM, S HONG. Green band generation for advan-ced baseline imager sensor using Pix2Pix with advanced baseline imager and advanced Himawari imager observations. IEEE Transactions on Geoscience and Remote Sensing, 59, 6415-6423(2021).

[32] P GONG, H LIU, M ZHANG et al. Stable classification with limited sample： Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64, 370-373(2019).

[33] M MIRZA, S OSINDERO. Conditional generative adversarial nets. Computer Science, 2672-2680(2014).

[34] O RONNEBERGER, P FISCHER, T BROX. U-Net：Convolutional networks for biomedical image segmentation(2015).