Recovering Thin Cloud Covered Regions in Gf Satellite Images Based on Cloudy Image Arithmetic +

Zunxiao Xu, Kang Wu, Peng Ren

Abstract: We propose the Cloudy Image Arithmetic + (CIA +) for training dataset construction of thin cloud removal, which addresses the deficiency of Cloud Image Arithmetic (CIA) that cloud shadows cannot be simulated. CIA + is able to synthesize cloudy images with cloud shadows, and as in nature, the angle and intensity of the cloud shadows vary depending on the irradiation angle and cloud thickness, which achieves state-of-the-art cloudy image synthesis. The first thin cloud removal dataset on GF satellite (TCR-GF) constructed with CIA + is released to supplement public data for cloud removal. Meanwhile, we propose the Dual-attention MSGAN to remove thin clouds. The network is capable to focus on thin cloud covered regions for the coordinate attention module encodes both channel relationship and long-range dependencies with precise position information. Several qualitative and quantitative experiments validate that the Dual-attention performs excellently on our TCR-GF dataset.