3D reconstruction of UAV remote sensing sequence image based on iterative constraint weighting

被引:0
作者
Sun T. [1 ,2 ]
Li M. [1 ]
Wang W. [1 ]
Liu C. [3 ]
机构
[1] Department of Mechanical and Electrical Engineering, Jiangsu Food and Pharmaceutical Science College, Jiangsu Province, Huai’an City
[2] School of Technology, Beijing Forestry University, Beijing
[3] College of Automotive Engineering, Huaian Vocational College of Information Technology, Jiangsu Province, Huai’an City
关键词
3D reconstruction; Iterative constraint weighting; Remote sensing sequence image;
D O I
10.1504/IJICT.2021.118574
中图分类号
学科分类号
摘要
Aiming at the problem that the traditional 3D reconstruction method of UAV remote sensing sequence images takes time and affects the reconstruction accuracy, a 3D reconstruction method of UAV remote sensing sequence images based on iterative constraint weighting is proposed. Construct a UAV remote sensing platform, and process the images of UAV remote sensing sequences through image enhancement, uniform light processing and stitching. An iterative constraint weighting method is introduced to solve the global rotation matrix problem as a rotation vector in algebra. Through the iterative constraint weighting method, the second programming obtains the optimal solution of the global position and optimises the global position and attitude. According to the position and attitude parameters of the acquired UAV remote sensing sequence image and the reconstruction point cloud, the 3D reconstruction of the image is realised. Experimental results show that the method is short, accurate, effective and reliable. Copyright © 2021 Inderscience Enterprises Ltd.
引用
收藏
页码:371 / 390
页数:19
相关论文
共 21 条
[1]  
Chen L.X., Simulation of optimization design in 3D space landscape image layout, Computer Simulation, 34, 28, pp. 297-300, (2017)
[2]  
Chen P., Dang Y., Liang R., Zhu W., Real-time object tracking on a drone with multi-inertial sensing data, IEEE Transactions on Intelligent Transportation Systems, pp. 1-9, (2017)
[3]  
Fu X., Huang K., Yang B., Ma W.K., Robust volume minimization-based matrix factorization for remote sensing and document clustering, IEEE Transactions on Signal Processing, 64, 53, pp. 6254-6268, (2016)
[4]  
Gao Q., Lim S., Jia X., Spectral-spatial hyperspectral image classification using a multiscale conservative smoothing scheme and adaptive sparse representation, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-13, (2019)
[5]  
Guo Y., Jia X., Paull D., Effective sequential classifier training for SVM-based multitemporal remote sensing image classification, IEEE Transactions on Image Processing, 27, 49, pp. 3036-3048, (2018)
[6]  
He D., Qiao Y., Chan S., Guizani N., Flight security and safety of drones in airborne fog computing systems, IEEE Communications Magazine, 56, pp. 66-71, (2018)
[7]  
Jing Y.B., Chen X.W., The UAV laser radar captures the reconstruction of remote sensing images, Laser Journal, 39, 36, pp. 157-160, (2018)
[8]  
Jing Y.B., Chen X.W., The UAV laser radar captures the reconstruction of remote sensing images, Laser Journal, 39, pp. 157-160, (2018)
[9]  
Kumar B., Dikshit O., Hyperspectral image classification based on profiles and decision fusion, International Journal of Remote Sensing, 38, 19, pp. 5830-5854, (2017)
[10]  
Lee S.Y., Kim H.G., Kim H.S., Choi J.H., Comparison of image enlargement according to 3D reconstruction in a CT scan: using an aneurysm phantom, Journal – Korean Physical Society, 72, 37, pp. 805-810, (2018)