针对小样本条件下遥感图像目标的检测精度受到卷积神经网络过拟合影响的问题,提出一种基于生成对抗网络的数据增强方法,利用判别模型为生成模型同时提供图像的局部决策与全局决策,以提高生成模型生成图像的质量,并将生成的目标与训练集图像进行融合得到新的样本,且新生成的样本不需人工标注。实验结果表明,在原始数据中加入生成数据后,检测识别精度有所提高,且本文方法与基于图像仿射变换的数据增强方法的叠加使用进一步提高了数据增强的效果。

To solve the problem that the detection accuracy of remote sensing image targets is affected by convolution neural network overfitting under the condition of small samples, a data augmentation method based on generative adversarial networks is proposed. The discrimination model is used to provide local and global decisions for the generation model to improve the quality of the image generated by the generative model. The new samples are obtained by fusing the generated target and the training set image, and the new samples do not need to be labeled manually. Experimental results show that: the accuracy of detection and recognition is improved after adding the generated data to the original data; this method can be superimposed with the data augmentation method based on image affine transformation to further improve the effect of data augmentation.