A Radiometric Block Adjustment Method for Unmanned Aerial Vehicle Images Considering the Image Vignetting

被引:2
|
作者
Peng, Wanshan [1 ]
Gong, Yan [1 ]
Fang, Shenghui [1 ]
Zhang, Yongjun [1 ]
Dash, Jadunandan [2 ]
Ren, Jie [1 ]
Mo, Jiacai [1 ]
机构
[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
[2] Univ Southampton, Sch Geog & Environm Sci, Southampton SO17 1BJ, England
关键词
Radiometry; Calibration; Reflectivity; Sensors; Lighting; Autonomous aerial vehicles; Roads; Block adjustment (BA); light-dark differences; radiometric calibration; unmanned aerial vehicles (UAVs); vignetting; EMPIRICAL LINE METHOD; CALIBRATION METHOD; UAV; REFLECTANCE; SYSTEMS; PLANTS;
D O I
10.1109/TGRS.2023.3268036
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Unmanned aerial vehicles (UAVs) equipped with different sensors can provide data with high spatiotemporal resolution and have broad application prospects. During the flight of the UAV, changes in illumination, exposure time, etc., will cause different degrees of radiometric differences between images, resulting in a calibration relationship established on a single image that cannot be applied to other images; in addition, the vignetting effect also significantly changes the brightness distribution inside an image, thus posing challenges for radiometric calibration of UAV images. In this article, based on block adjustment (BA), we proposed a radiometric BA model under the consideration of vignetting and the light-dark differences between images. The proposed method requires only a small number of calibration blankets, thus reducing the complexity of the experiment. The results from two study areas showed that the proposed method could compensate for vignetting to a certain extent and the radiometric consistency of the two datasets was improved from 12.9%-21.8% to 4.7%-12.7%. Validated using ground samples, the mean root mean square error (RMSE) and mean relative percent error (MRPE) of all five bands were 0.054, 21.8%, and 0.037, 20.4% in the two study areas, respectively. The total uncertainty was less than 8.1%. When there were obvious light-dark differences between images, such as in the visible light bands, our method could significantly improve the accuracy of the radiometric calibration.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment
    Honkavaara, Eija
    Khoramshahi, Ehsan
    REMOTE SENSING, 2018, 10 (02):
  • [2] Method of radiometric quality assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle
    Wierzbicki, Damian
    Fryskowska, Anna
    Kedzierski, Michal
    Wojtkowska, Michalina
    Delis, Paulina
    JOURNAL OF APPLIED REMOTE SENSING, 2018, 12
  • [3] A Mosaic Method on Images Small of Unmanned Aerial Vehicle
    Gao, Xiang
    PROCEEDINGS OF THE 2016 2ND WORKSHOP ON ADVANCED RESEARCH AND TECHNOLOGY IN INDUSTRY APPLICATIONS, 2016, 81 : 510 - 513
  • [4] An Efficient Method for The Unmanned Aerial Vehicle Image Denoising
    Liu, Fang
    Deng, Zhiren
    2013 IEEE 4TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC), 2014, : 213 - 216
  • [5] MACA: A Relative Radiometric Correction Method for Multiflight Unmanned Aerial Vehicle Images Based on Concurrent Satellite Imagery
    Jiang, Jiale
    Zhang, Qiaofeng
    Wang, Wenhui
    Wu, Yapeng
    Zheng, Hengbiao
    Yao, Xia
    Zhu, Yan
    Cao, Weixing
    Cheng, Tao
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [6] Algorithm for automatic image dodging of unmanned aerial vehicle images using two-dimensional radiometric spatial attributes
    Li, Wenzhuo
    Sun, Kaimin
    Li, Deren
    Bai, Ting
    JOURNAL OF APPLIED REMOTE SENSING, 2016, 10
  • [8] Automatic Car Counting Method for Unmanned Aerial Vehicle Images
    Moranduzzo, Thomas
    Melgani, Farid
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (03): : 1635 - 1647
  • [9] Unmanned aerial vehicle aerial image stitching method based on superpixel segmentation
    Zhiyou Lian
    Jianhua Ren
    Journal of Engineering and Applied Science, 2025, 72 (1):
  • [10] Experimental tests of image fusion method for unmanned aerial vehicle
    Zhao, F. (zhaofuli35022412@126.com), 1600, Advanced Institute of Convergence Information Technology (04):