SAR Image Data Augmentation via Residual and Attention-Based Generative Adversarial Network for Ship Detection

Abstract

In recent years, generative adversarial networks (GANs) have been successfully applied to generate the SAR images. However, due to the fact that it is more difficult to generate the images than to distinguish the real or fake, GANs usually suffer from the problems of unstable training and mode collapse. As such, a residual and attention-based generative adversarial network (RAGAN) is proposed for SAR data augmentation. Firstly, the directional bounding box is used as a constraint in the RAGAN to limit the position of ship in the generated SAR image, which can be further set as the annotation of the SAR image for ship detection directly. After that, inspired by the residual and attention learning, a residual and attention block (RABlock) and a transposed RABlock (TRABlock) are designed to improve the generator of the RAGAN, thus preventing the whole model from gradient vanishing and suppressing the effects of speckle noise and background to enhance the quality of the generated SAR images. Experimental results on the HRSID data set demonstrate the effectiveness of our RAGAN model in SAR data augmentation for ship detection.