Application of UAV Remote Sensing Technology in Forest Fire Monitoring Based on Retinex Theory Algorithm
DOI: https://doi.org/10.62517/jbdc.202601114
Author(s)
Boyu Liu
Affiliation(s)
Department of Mechanical Enginnering, School of Engineering, Xi'an Aeronautical University, Xi'an, Shaanxi, China
*Corresponding Author
Abstract
This paper discusses the application of UAV remote sensing technology based on Retinex theory algorithm in forest fire monitoring. Firstly, the importance of forest fire detection and the limitation of traditional monitoring methods are introduced. Secondly, the advantages of UAV remote sensing technology are explained, including flexibility, efficiency and accuracy. Retinex theory algorithm is analyzed in detail in enhancing image quality, solving complex illumination processing, color vector and other core problems. The effectiveness and accuracy of this technology combination in forest fire monitoring are demonstrated through practical cases and experimental data. Finally, the future development direction and prospect of this technology are prospected.
Keywords
Retinex Theory Algorithm; UAV Remote Sensing Technology; Forest Fire Monitoring
References
[1] Zheng Dichen, He Jikai, Liu Yi, et al. Adaptive enhancement algorithm for low-illumination images based on Retinex theory [J/OL]. Computer Science, 1-10 [2025-08-25]. www.example.com.
[2] Ren Bin. Research and Implementation of Image Enhancement Algorithm Based on Retinex [D]. Nanjing University of Science and Technology, 2009.
[3] Yan Baozhong, Han Xudong, He Wei. An Improved Low-illumination Image Enhancement Algorithm Based on Retinex Theory [J]. Applied Science, 2020(005):047.
[4] Tu Juanjuan. Low illumination image and video enhancement algorithm based on Retinex theory under complex illumination conditions [D]. Nanjing University of Posts and Telecommunications,2020.DOI:10.27251/d.cnki.gnjdc.2020.001248.
[5] Li Yijian. Research on Preprocessing and Mosaicing Technology of Multispectral Image from UAV Low Altitude Remote Sensing[D]. ZhejiangUniversity,2019.
[6] Liu Jiangshan, Cang Yan. Low light image enhancement algorithm based on Retinex theory [J]. Applied Science, 2024, 51 (05):249-255.
[7] Zhang Enqi, Kong Lingsheng, Guo Junda, et al. Low illumination image contrast enhancement algorithm based on Retinex theory [J]. Electromechanical Engineering Technology, 2022, 51 (03):95-98+144.
[8] Liu Tao, Ge Dapeng, Zhang Yanhui. Practice and challenge of UAV remote sensing technology in forest fire monitoring and extinguishing [J]. Forestry Industry in China, 2025,(04):50-51.