Data Augmentation in SAR Images Based on Multi-Scale Generative Adversarial Networks

To solve the problem that it is difficult for military unmanned aerial vehicles to acquire synthetic aperture radar images of important ships at sea, this paper introduces an unconditional image generation network which can learn the internal distribution of images from a single image. The network adopts the idea of a pyramid of multi scale generative adversarial networks (GAN). In each layer of pyramid, there is a GAN responsible for the generation and discrimination of image blocks at this scale, and each GAN has a similar structure. The head of generator contains Inception modules connected with different sizes of convolution kernels to obtain image features at different scales. In order to make full use of these features, a residual dense block is added. The discriminator uses the idea of Markov discriminator to capture images distribution at different scales. All the generated images arc made into data sets for training different target detection algorithms, the results show that the average accuracy of the model is improved to a certain extent, which verifies the effectiveness of the network model.