关键词: 图像转换算法, 生成对抗网络, 遥感图像去云, VGG损失函数

Abstract: Many problems in computer vision can be abstracted as “converting” an input image into a corresponding output image, which is a general solution to many computer vision problems, such as semantic segmentation, image style transfer, etc. In this paper, the remote sensing image cloud removal is used as the special case of image conversion, and the image conversion algorithm based on Generative Adversarial Networks (GANs) is studied. The GANs based on the residual module is proposed to remove the thick cloud and thin cloud from single remote sensing image. At the same time, the proposed multi-scale discriminator and VGG loss function can effectively deal with the cloud occlusion problem of complex scenes. The experimental results show that the proposed image conversion algorithm increases the peak signal-to-noise ratio on the remote sensing image thin cloud dataset by 1.64 dB and increases the peak signal-to-noise ratio by 1.92 dB on the thick cloud dataset. At the same time, the generated cloud-free remote sensing images have high structural similarity with the real cloud-free images.

Key words: image-to-image translation, generative adversarial networks, remote sensing image cloud removal, VGG loss function