  Optical remote sensing imagery has been widely used in many fields due to its
high resolution and stable geometric properties. However, remote sensing
imagery is inevitably affected by climate, especially clouds. Removing the
cloud in the high-resolution remote sensing satellite image is an indispensable
pre-processing step before analyzing it. For the sake of large-scale training
data, neural networks have been successful in many image processing tasks, but
the use of neural networks to remove cloud in remote sensing imagery is still
relatively small. We adopt generative adversarial network to solve this task
and introduce the spatial attention mechanism into the remote sensing imagery
cloud removal task, proposes a model named spatial attention generative
adversarial network (SpA GAN), which imitates the human visual mechanism, and
recognizes and focuses the cloud area with local-to-global spatial attention,
thereby enhancing the information recovery of these areas and generating
cloudless images with better quality...
