HIGH RESOLUTION SAR IMAGE SYNTHESIS WITH HIERARCHICAL GENERATIVE ADVERSARIAL NETWORKS

Generative adversarial network (GAN) is an artificial neural network based on unsupervised learning method. Due to its powerful model representation capabilities, GAN has been introduced to synthesize synthetic aperture radar (SAR) image data, for the real sample is difficult to acquire. Large-scale, high-resolution SAR images play an important role in promoting SAR applications, such as automatic target recognition and image interpretation. However, on account of the difficult training problem of GAN network, especially for SAR images with speckle noise, it is difficult to obtain high-resolution SAR images by simply transfer the net from optical image. Recent studies in other image fields have shown that hierarchical structure is an effective and useful way to decompose a generation task into several smaller subtasks. How to obtain more high-resolution SAR images from limited original samples through GAN is the target of our research. Therefore, in this paper, we introduce a hierarchical GAN network model to generate SAR images, through the multi-stage network, gradually improve the quality of the generated image, and finally obtain high-resolution images. The type and aspect of generated images are determined by the input of condition vectors in the last two stages. In addition, we introduce the triple loss, in which the background loss is used to imitating background clutter noise of SAR image, the condition loss is to make the generated images' type and aspect become controllable, and the global loss for getting higher image generation quality. The generated images show high similarity with the real samples.