Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

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