Attentive generative adversarial network for removing thin cloud from a single remote sensing image

Land-surface observation is easily affected by the light transmission and scattering of semi-transparent clouds, high or low, resulting in blurring and reduced contrast of ground objects. To improve the visual appearance of remote sensing images, the authors present a deep learning method for thin cloud removal using a new attentive generative adversarial network without prior knowledge or assumptions, which copes with thin clouds that are unevenly distributed on different images and learns the attention map with weighted information about spatial features. Such a spatial attention model can endow each pixel with the global spatial context information. Consequently, the generative network focuses on the thin cloud regions to generate better local image restoration, and the discriminative network can evaluate the local consistency of the repaired regions. The experimental results show that this method is superior to state-of-the-art methods in recovering detailed texture information.

Automated summary

The paper introduces a deep learning method for thin cloud removal using a new attentive generative adversarial network, which effectively deals with thin clouds in land-surface observation images and improves the visual appearance of remote sensing images. Experimental results show that this method significantly outperforms existing approaches in recovering detailed texture information.