Abstract
We propose a method to establish super-resolution (SR) virtual scenes based on multi-spectral remote sensing images. Multi-spectral remote sensing images are processed, then the SR scenes are realized based on the convolutional neural network (CNN) with a special training set. The results show that the training set proposed in this paper can improve the generalization ability of CNN in processing remote sensing images, and different operation sequences can significantly affect the restoration quality of images. The terrain model is established in the physics engine based on the elevation data. Moreover, the flight simulation software with real-time SR virtual scenes is designed and implemented.
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Acknowledgements
This research was funded by the National Natural Science Foundation of China (Grant No. 71971127), Guangdong Pearl River Plan (2019QN01X890), and Shenzhen Science and Technology Innovation Commission (JCYJ20210324135011030).
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Li, J., Chan, W.K. (2023). Super-Resolution Virtual Scene of Flight Simulation Based on Convolutional Neural Networks. In: Tang, L.C., Wang, H. (eds) Big Data Management and Analysis for Cyber Physical Systems. BDET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-031-17548-0_13
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