Authors:
Leandro Silva
1
;
2
;
João Mari
1
;
Mauricio Escarpinati
2
and
André Backes
3
Affiliations:
1
Institute of Exact and Technological Sciences, Federal University of Viçosa - UFV, Rio Paranaíba-MG, Brazil
;
2
School of Computer Science, Federal University of Uberlândia, Uberlândia, Brazil
;
3
Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil
Keyword(s):
Cloud Removal, Diffusion Model, Remote Sensing, Optimizers, Loss Functions.
Abstract:
Cloud removal is crucial for photogrammetry applications, including urban planning, precision agriculture, and climate monitoring. Recently, generative models, especially those based on latent diffusion, have shown remarkable results in high-quality synthetic image generation, making them suitable for cloud removal tasks. These approaches require optimizing numerous trainable parameters with various optimizers and loss functions. This study evaluates the impact of combining three optimizers (SGD, Adam, and AdamW) with the MAE, MSE, and Huber loss functions. For evaluation, we used the SEN MTC New dataset, which contains pairs of 4-band images with and without clouds, divided into training, validation, and test sets. The results, measured in terms of PSNR and SSIM, show that the diffusion model combining AdamW and the Huber loss function delivers exceptional performance in cloud removal.