Study on the application of UAV intelligent dehazing in landscape planning and design

Lili Zhang; Youqiang Zhang; Heping Long

doi:10.1117/12.2675339

25 May 2023 Study on the application of UAV intelligent dehazing in landscape planning and design

Lili Zhang, Youqiang Zhang, Heping Long

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263647 (2023) https://doi.org/10.1117/12.2675339
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In the field of landscape planning and design, Unmanned Aerial Vehicles (UAV) technology plays an increasingly important role. However, in hazing environment, the images acquired by drones affect not only visual appreciation, but also the efficiency the efficiency of landscape planning and design. Aiming at the above problems, this paper presents an intelligent method of UAV image based on adaptive convolution. Firstly, according to the inhomogeneity of image degradation in hazing environment, an adaptive convolution module is proposed to intelligently extract projection images. Secondly, the multi-scale processing module learns the feature information of the clear image from the degraded image; Finally, the unsupervised framework is combined with the imaging model, and the traditional data set-driven mode is cancelled, so as to better output clear images. The results show that the proposed intelligent dehazing method of UAV images can better deal with the real environment and improve the quality of degraded images in hazing environments. The subjective and objective evaluation indicators of this algorithm are better than other comparison algorithms.

Citation Download Citation

Lili Zhang, Youqiang Zhang, and Heping Long "Study on the application of UAV intelligent dehazing in landscape planning and design", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263647 (25 May 2023); https://doi.org/10.1117/12.2675339

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KEYWORDS

Unmanned aerial vehicles

Image processing

Atmospheric modeling

Convolution

Design and modelling

Image quality

Data modeling

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