  Deep learning approaches require enough training samples to perform well, but
it is a challenge to collect enough real training data and label them manually.
In this letter, we propose the use of realistic synthetic data with a wide
distribution to improve the performance of remote sensing image aircraft
detection. Specifically, to increase the variability of synthetic data, we
randomly set the parameters during rendering, such as the size of the instance
and the class of background images. In order to make the synthetic images more
realistic, we then refine the synthetic images at the pixel level using
CycleGAN with real unlabeled images. We also fine-tune the model with a small
amount of real data, to obtain a higher accuracy. Experiments on NWPU VHR-10,
UCAS-AOD and DIOR datasets demonstrate that the proposed method can be applied
for augmenting insufficient real data.
