Abstract
This paper presents a multi-sequence satellite image removal algorithm to remove cloud cover in remote sensing images. The algorithm is based on dual residual network. Above all, the network uses multi-temporal cloud images as the input of the model. Then, it fuses the global and local feature information through the dual residual connection structure, which makes the overall structure of the generated restored image reasonable and the edge details clearer. Finally, it uses pixel up-sampling to enhance the utilization of spatial information and improve the restoration effect. Experiments on the European Sentinel-2 remote sensing satellite image dataset with different cloud removal algorithms demonstrate that our network achieves higher quality than the state-of-the-art methods. The proposed method was verified on the dataset, and the PSNR and SSIM indexes were 27.04 and 0.8479, both exceeding the original processing method STGAN of the dataset, and improving the effectiveness of remote sensing image cloud removal.
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Xiao, C., Wu, X. (2022). Multi-sequence Satellite Image Cloud Removal Based on Dual Residual Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_16
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DOI: https://doi.org/10.1007/978-3-031-06788-4_16
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