Affiliation:
1. Tomsk Polytechnic University
2. Tomsk State University of Control Systems and Radioelectronics
3. German Aerospace Centre
Abstract
The goal of this research was to study and optimize multi-temporal RGB images obtained by a UAV (unmanned aerial vehicle). A digital camera onboard the UAV allows obtaining data with a high temporal and spatial resolution of ground objects. In the case considered by us, the object of study is agricultural fields, for which, based on numerous images covering the agricultural field, image mosaics (orthomosaics) are constructed. The acquisition time for each orthomosaic takes at least several hours, which imposes a change in the illuminance of each image, when considered separately. Orthomosaics obtained in different periods of the year (several months) will also differ from each other in terms of illuminance. For a comparative analysis of different parts of the field (orthomosaic), obtained in the same time interval or comparison of areas for different periods of time, their alignment by illumination is required. Currently, the majority of alignment approaches rely rather on colour (RGB) methods, which cannot guarantee finding efficient solutions, especially when it is necessary to obtain a quantitative result. In the paper, a new method is proposed that takes into account the change in illuminance during the acquisition of each image. The general formulation of the problem of light correction of RGB images in terms of assessing the colour vegetation index Greenness is considered. The results of processing real measurements are presented.
Publisher
Redakcia Zhurnala Svetotekhnika LLC
Reference16 articles.
1. Zhuravlev V.N., Zhuravlev P.V. The use of unmanned aerial vehicles in sectors of the economy: state and prospects. // Scientific Bulletin of MSTU GA, 2016, # 226, pp. 156–164.
2. Candiago S., Remondino F., DE Giglio M., Dubbini M. Gattelli M. (2015) Evaluating multispectral images and vegetation indices for precision farming applications from UAV images// Remote sensing, 2015,# 7, pp. 4026–4047.
3. Mogili U.R ., Deepak B. Review on application of drone systems in precision agriculture// Procedia Comput. Sci. 2018, #133, pp. 502–509.
4. Brede B., Suomalainen J., Bartholomeus H., Herold M. Influence of solar zenith angle on the enhanced vegetation index of a Guyanese rainforest// Remote Sens. Lett. 2015, #6, pp. 972–981.
5. Jakob S., Zimmermann R., Gloaguen R. The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo – A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data// Remote Sens. 2017, # 9, 88p.
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献