DE-CycleGAN: An Object Enhancement Network for Weak Vehicle Detection in Satellite Images

Vehicle detection is a very important application of remote sensing. However, suffering from the low acutance and insufficient color information, the detection of weak vehicles in satellite imagery still remains a challenge. Image enhancement can improve the visual effects of remote sensing images. Nevertheless, most existing image enhancement methods aim to improve the quality of the entire image without target guidance, which have ambiguous contributions to the detection performance. Methods based on generative adversarial networks (GANs) have realized image enhancement with target guidance by the addition of target-guided branches, but paired training data is not available in some scenarios. In this article, a novel model of detection-guided CycleGAN (DE-CycleGAN) is proposed to enhance the weak targets for the purpose of accurate vehicle detection, where a backbone GAN with a target-guided branch is learned in the absence of paired images. Specifically, enhancements of two levels are mutually executed. At the image level, the color information of the entire satellite image is enriched by refined CycleGAN, and its sharpness is enhanced by the gradient enhancement model. At the object level, the target-guided branch for detection is added to enhance features of the target. The experimental results validate that the detection performance has been significantly improved on the images enhanced by the proposed DE-CycleGAN model, which shows a positive effect on weak target detection.