Remoção de nuvens e reconstrução de imagens de satélite usando deep learning

Abstract

Remote sensing has proven to be an essential tool for fundamental applications such as environmental preservation, monitoring and prevention of natural disasters, urban planning, and precision agriculture. Satellites are preponderant data acquisition sources for remote sensing due to their ability to offer high-quality images in several spectral bands. However, satellite images can be sensitive to artifacts, such as clouds, shadows, and snow, which interfere with the mentioned applications. Therefore, this work presents four main contributions. (i) A systematic literature review, establishing a taxonomy, challenges, perspectives, and trends for research related to cloud removal. (ii) This work presents a new latent diffusion approach with residual blocks and combined attention channels for cloud removal, reducing the number of parameters by 48\% to 82\% compared to the state-of-the-art while preserving reconstruction quality in quantitative and qualitative terms. In addition, (iii) investigated how the interaction between optimizers and loss functions influences the learning process for reconstructing cloudy areas through a latent diffusion model. Finally, (iv) this work presents a comprehensive study of the behavior and impact of different architectures and segmentation models for a satellite imagery scenario with clouds.