Light & Engineering 27 (5)

Volume 27
Pages 55-62

Method of Vegetation Detection Using RGB Images Made by Unmanned Aerial Vehicles on the Basis of Colour and Texture Analysis. L&E 27 (5) 2019

Articles authors:

Mikhail Yu. Kataev, Maria M. Dadonova

Mikhail Yu. Kataev Dr. of Tech. Sci., Professor of the Department of Automated Control Systems (ACS) and Scientific Supervisor of the Centre for Space Monitoring of the Earth from Space TUSUR, Tomsk State University of Control Systems and Radio-Electronics

Maria M. Dadonova, master’s student at the Department of Automated Control Systems (ACS) TUSUR

Abstract:

The article describes the capability of application of RGB?1 images made by digital cameras for detection of Earth surface (vegetation) types. A set of measures necessary to be taken for processing of the images made by unmanned aerial vehicles (UAV) in real-time is described. Application of analogue of vegetation index allows detecting vegetation on an RGB image, which increases the probability of correct detection of surface (vegetation) type. Methods of preliminary and thematic processing of images required for positive detection of surface types are considered. Texture analysis is applied for detection of vegetation type. The results of the processing of real images are provided.

References:

1. Kashkin V.B. Distantsionnoe zondirovanie Zemli iz kosmosa. TSifrovaia obrabotka izobrazhenii: ucheb. posobie [Remote Sensing of Earth from Outer Space. Digital Processing of Images: study guide] // Logos, Moscow, 2001, 264 p.
2. Chandra A.M., Gosh S.K. Distantsionnoe zondirovanie i geograficheskie informatsionnye sistemy [Remote Sensing and Geographic Information Systems] // Tekhnosphera, Moscow, 2008, 312 p.
3. Fetisov V.S. Bespilotnaia aviatsiia: terminologiia klassifikatsiia sovremennoe sostoianie [Unmanned Aviation: Terminology, Classification, Contemporary Development] // Photon, Ufa, 2014, 217 p.
4. Starovoytov V.V., Golub Yu.I. Tsifrovye izobrazheniia ot polucheniia do obrabotki [Digital Images: from Acquiring to Processing] // OIPI NAN, Minsk, 2014, 202 p.
5. Yanshin V.V., Kalinin G.A. Obrabotka izobrazhenii na iazyke Si dlia IBM PC: Algoritmy i programmy [Processing Images on C for IBM PC: Algorithms and Programmes] // Mir, Moscow, 1994, 240 p.
6. Pratt W. Tsifrovaia obrabotka izobrazhenii [Digital Processing of Images] // Mir, Moscow, 1982, Vol. 1-2, 1104 p.
7. Shapiro L., Stockman J. Kompiuternoe zrenie [Computer Vision] // Binom, Laboratoria Znany, Moscow, 2006, 752 p.
8. Visilter Yu.V., Zheltov S. Yu., Bondarenko A.V., Ososkov M.V., Morzhin A.V. Obrabotka i analiz izobrazhenii v zadachakh mashinnogo zreniia [Processing and Analysis of Images in Computer Vision Problems] // Fizmatkniga, Moscow, 2010, 672 p.
9. Hruska R., Mitchell J., Anderson M., Glenn N.F. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle // Remote Sens, 2012, No. 4, pp. 2736-2752.
10. Shepherd J.D., Dymond J.R. Correcting satellite imagery for the variance of reflectance and illumination with topography // Int. J. Remote Sens, 2003, Vol. 24, pp. 3503-3514.
11. Colomina I., Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review // ISPRS J. Photogramm. Remote Sens, 2014, Vol. 92, p. 79-97.
12. Huang W., Zhang L., Li P.-X. A Topographic Correction Approach for Radiation of RS Images by Using Spatial Context Information // Acta Geodaetica Et Cartographica Sinica, 2006, Vol. 35, No. 8, pp. 286-290.
13. Dybayah R., Rich P. Topographic solar radiation models for GIS // International Journal of Geographical Information Systems, 1995, No. 9, pp. 405-419.
14. Bolstad P.V., Smith J.L. An Evaluation of DEM Accuracy: Elevation, Slope Aspects // Photographic Engineering and Remote Sensing, 1994, Vol. 60, No. 11, pp. 1327-1332.
15. Toutin T. Geometric Processing of Remote Sensing Images: Models, Algorithms and Methods // International Journal of Remote Sensing, 2004, Vol. 25, No. 10, pp. 1893-1924.
16. Kim J., Kim T. Fast and robust geometric correction for mosaicking UAV images with narrow overlaps // International Journal of Remote Sensing, 2017, Vol. 38, No. 8-10, pp. 2557-2576.
17. Reinhard E., Adhikhmin M., Gooch B., Shirley P. Color transfer between images // IEEE Computer graphics and applications, 2001, Vol. 21, No. 5, pp. 34-41.
18. Meyer G.E., Neto C.J. Verification of color vegetation indices for automated crop imaging applications // Computers and electronics in agriculture, 2008, Vol. 63, No. 2, pp. 282-293.
19. Bannari A., Morin D., Bonn F., Huete A.R. A review of vegetation indices // Remote Sensing Reviews, 1995, Vol. 13, pp. 95-120.
20. URL: http://phl.upr.edu/projects/visible- vegetation-index-vvi.
21. URL: https://colorscheme.ru.

DOI: https://doi.org/10.33383/2018-078