Abstract
In order to improve the target recognition effect of small UAV (unmanned aerial vehicle) remote sensing image, this paper proposes a new super-resolution reconstruction method based on the recurrent convolutional network, which can achieve different degrees of super-resolution effect by controlling the number of cycles. Moreover, it can control the number of iterations of small UAVs with different degrees of blur and can be better adapted to the recognition scenarios of UAVs. In addition, this paper studies the target recognition method of small UAV remote sensing image, combines fuzzy clustering method to construct the intelligent remote sensing image target recognition model, combines it with the UAV structure, realizes remote sensing recognition by UAV, and designs experiments to analyze the effect of remote sensing recognition. Further, this paper improves the recognition algorithm and positioning algorithm of remote sensing image, so that recognition and positioning of UAV video remote sensing image can get better results. Finally, this paper verifies the performance of the system through simulation experiments. The research results show that the method proposed in this paper has certain reliability.
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The research is supported by Jilin Education Department"135"Science and Technology (No. JJKH20190543KJ).
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Guo, L., Yang, R., Zhong, Z. et al. Target recognition method of small UAV remote sensing image based on fuzzy clustering. Neural Comput & Applic 34, 12299–12315 (2022). https://doi.org/10.1007/s00521-021-06650-y
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DOI: https://doi.org/10.1007/s00521-021-06650-y