A Parallel Attention-Guided Generative Adversarial Network for Efficient Thin Cloud Removal From Satellite Imagery

Abstract

Earth observation depends on the spatial-temporal data from satellites. Optical observations are often affected by random thin clouds. This cloud interference impacts the usefulness of satellite-based remote sensing in several application areas. This paper introduces a cloud-free reconstruction architecture based on a Generative Adversarial Network (GAN) that leverages spatial-attention mechanisms. The proposed Parallel Attention Guided Generative Adversarial Network for Efficient Thin Cloud Removal (PACR-GAN) integrates the benefits of the Convolutional Block Attention Module (CBAM) and the Coordinate Attention Module (CAM). When tested against the RICE-1 dataset, the proposed model demonstrated superior performance in terms of popular evaluation metrics when compared to existing methods. The model effectively reconstructed cloud-free images by focusing on critical features and spatial details, showing resilience to thin clouds.