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
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2023年 / 61卷
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
  • [31] A Method for Managing the Route of an Unmanned Aerial Vehicle
    Shvetsov A.V.
    Shvetsova S.V.
    Russian Aeronautics, 2021, 64 (01): : 142 - 145
  • [32] Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle
    Hruska, Ryan
    Mitchell, Jessica
    Anderson, Matthew
    Glenn, Nancy F.
    REMOTE SENSING, 2012, 4 (09) : 2736 - 2752
  • [33] Block Matching Based Obstacle Avoidance for Unmanned Aerial Vehicle
    Ivanovas, Adomas
    Ostreika, Armantas
    Maskeliunas, Rytis
    Damasevicius, Robertas
    Polap, Dawid
    Wozniak, Marcin
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2018, PT I, 2018, 10841 : 58 - 69
  • [34] Lightweight vehicle object detection network for unmanned aerial vehicles aerial images
    Liu, Lu-Chen
    Jia, Xiang-Yu
    Han, Dong-Nuo
    Li, Zhen-Dong
    Sun, Hong-Mei
    JOURNAL OF ELECTRONIC IMAGING, 2023, 32 (01)
  • [35] Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform
    Xiang, Haitao
    Tian, Lei
    BIOSYSTEMS ENGINEERING, 2011, 108 (02) : 104 - 113
  • [36] Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment
    Miyoshi, Gabriela Takahashi
    Imai, Nilton Nobuhiro
    Garcia Tommaselli, Antonio Maria
    Honkavaara, Eija
    Nasi, Roope
    Saito Moriya, Erika Akemi
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (15-16) : 4910 - 4930
  • [37] Mismatches Removal in Unmanned Aerial Vehicle Image Registration using Density Based Method
    Ye, Yunfei
    Luo, Zijuan
    Yu, Xuelian
    Ren, Kan
    Gao, Yuan
    Chen, Qian
    AOPC 2021: INFRARED DEVICE AND INFRARED TECHNOLOGY, 2021, 12061
  • [38] Unmanned aerial vehicle (UAV) images of road vehicles dataset
    Mustafa, Nama Ezzaalddin
    Alizadeh, Fattah
    DATA IN BRIEF, 2024, 54
  • [39] Estimating tree heights with images from an unmanned aerial vehicle
    Birdal, Anil Can
    Avdan, Ugur
    Turk, Tarik
    GEOMATICS NATURAL HAZARDS & RISK, 2017, 8 (02) : 1144 - 1156
  • [40] Classification and Segmentation of Watermelon in Images Obtained by Unmanned Aerial Vehicle
    Ekizi, Ahmet
    Arica, Sami
    Bozdogan, Ali Musa
    2019 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO 2019), 2019, : 619 - 622