Single Satellite Optical Imagery Dehazing using SAR Image Prior Based on conditional Generative Adversarial Networks

Binghui Huang, Li Zhi, Chao Yang, Fuchun Sun, Yixu Song; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1806-1813

Satellite image dehazing aims at precisely retrieving the real situations of the obscured parts from the hazy remote sensing (RS) images, which is a challenging task since the hazy regions contain both ground features and haze components. Many approaches of removing haze focus on processing multi-spectral or RGB images, whereas few of them utilize multi-sensor data. The multi-sensor data fusion is significant to provide auxiliary information since RGB images are sensitive to atmospheric conditions. In this paper, a dataset called SateHaze1k is established and composed of 1200 pairs clear Synthetic Aperture Radar (SAR), hazy RGB, and corresponding ground truth images, which are divided into three degrees of the haze, i.e. thin, moderate, and thick fog. Moreover, we propose a novel fusion dehazing method to directly restore the haze-free RS images by using an end-to-end conditional generative adversarial network(cGAN). The proposed network combines the information of both RGB and SAR images to eliminate the image blurring. Besides, the dilated residual blocks of the generator can also sufficiently improve the dehazing effects. Our experiments demonstrate that the proposed method, which fuses the information of different sensors applied to the cloudy conditions, can achieve more precise results than other baseline models.