Journal of System Simulation
Abstract
Abstract: Aiming at the situation that the actual image of enemy reconnaissance equipment is difficult to be obtained, and the process of using similar equipment of our army is complicated and expensive in the detection of the camouflage effect of our important military targets, an equivalent detection method of using the UAV aerial photography to generate the high-altitude optical reconnaissance images is proposed. The number of pixels in the equivalent image is determined by comparing the acquisition height and the optical imaging device’s parameters of the UAV and the high-altitude reconnaissance equipment. The compressed pixel value is calculated through the interpolation. The pixel value is corrected according to the atmospheric transmission loss model and the equivalent high-altitude reconnaissance image is finally obtained. The comparison experiments show that the gray scale similarity between the original image and the equivalent generated image is 92.87%, which proves the feasibility of the method.
Recommended Citation
Tu, Jiangang; Cheng, Xu; Hui, Wang; and Shen, Zenghui
(2021)
"An Equivalent Method of UAV Simulating High-altitude Reconnaissance Equipment Optical Imaging,"
Journal of System Simulation: Vol. 33:
Iss.
10, Article 24.
DOI: 10.16182/j.issn1004731x.joss.20-0500
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss10/24
First Page
2511
Revised Date
2020-09-28
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0500
Last Page
2517
CLC
TN203;TP391
Recommended Citation
Tu Jiangang, Xu Cheng, Wang Hui, Shen Zenghui. An Equivalent Method of UAV Simulating High-altitude Reconnaissance Equipment Optical Imaging[J]. Journal of System Simulation, 2021, 33(10): 2511-2517.
DOI
10.16182/j.issn1004731x.joss.20-0500
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons