Adaptive Conditional GAN based Ka-Band PolSAR Image Simulation by Using X-Band PolSAR Image Transfer

Multi-band polarimetric synthetic aperture radar (PolSAR) has significant advantage in information extraction. However, the demanding acquisition requirement greatly prohibits its development. Typically, compared to low-frequency band PolSAR data, high-frequency band suffers more severe data insufficiency. In this paper, the authors proposed to resolve this issue by simulating Ka-band PolSAR images from X-band images. For this purpose, a conditional Generative Adversial Network (cGAN) based X-to-Ka band PolSAR image transfer network has been proposed. Adaptations in terms of preprocessing and loss function are made to the original cGAN so that it can be better adapted to PolSAR image processing. The proposed method is verified using the X- and Ka-band dataset acquired in Hainan, China by the Aerial Remote Sensing System of the Chinese Academy of Sciences. Experimental results demonstrate the feasibility of the proposed method.