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

TL;DR

This paper introduces SpA GAN, a novel neural network model that employs spatial attention within a generative adversarial framework to effectively remove clouds from high-resolution remote sensing images, improving image quality.

Contribution

The paper proposes a new spatial attention GAN model specifically designed for cloud removal in remote sensing imagery, integrating human visual-inspired attention mechanisms for enhanced performance.

Findings

SpA GAN outperforms existing methods in cloud removal quality.

The model effectively focuses on cloud regions, improving information recovery.

Generated images have higher clarity and fewer artifacts.

Abstract

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...