Analysis of the adequacy of gamma-correction of the shading effect in spectral surveys of the earth's surface using unmanned aerial vehicles


DOI: 10.34759/trd-2023-130-22

Аuthors

Gumbatov D. A.

National Aerospace Agency of Azerbaijan Republic, NASA, 1, Suleyman Sani Akhundov str., Baku, AZ1115, Azerbaijan Republic

e-mail: h.dilan@mail.ru

Abstract

The effect of shading in the real scene under study should be taken into account when studying the state of vegetation using various vegetation indices. For example, the values of such widely used indices as NDVI and LAI are slightly higher in sunny areas compared to shaded areas. The shading effect can also provide useful information of a geometric nature about the location of remotely studied objects in the environment. The object of the study is the effect of shading during spectral surveys of the earth using UAVs equipped with spectral cameras. The subject of the study is the adequacy of the gamma-correction of the shading effect, carried out during spectral surveys. The purpose of the study is to study the degree of adequacy of the gamma correction method for objects that are partially shaded. A significant difference is shown in the degree of adequacy of the γ-correction as applied to objects of the same type with an identical degree of shading in the case of applying the methods of geometric and algebraic averaging. The average value of DN can generally be calculated in two ways: 1. Geometric averaging method. 2. Method of algebraic (convolutional) averaging The difference found is that in the case of geometric averaging, the adequacy of the γ correction is understood in the sense of equality of the average value of the logarithm of the corrected value of the geometric averaging of the shaded and unshaded parts of objects to the average value of the logarithm DN for the unshaded part. However, in the case of algebraic averaging, the adequacy of γ correction is understood in the sense of equality of the average value of the logarithm DN of the unshaded part to the average value of the logarithm of the γ-corrected value DN of the shaded part of objects.

Keywords:

UAV, correction, shading effect, multispectral and hyperspectral cameras, remote sensing

References

  1. Antonov D.A., Zharkov M.V., Kuznetsov I.M., Lunev E.M., Pron’kin A.N. Trudy MAI, 2016, no. 91. URL: https://trudymai.ru/eng/published.php?ID=75632
  2. Korneev M.A., Maksimov A.N., Maksimov N.A. Trudy MAI, 2012, no. 58. URL: https://trudymai.ru/eng/published.php?ID=33061
  3. Volokitin D.A., Knyazeva V.V., Rumyantsev D.S. Trudy MAI, 2015, no. 83. URL: https://trudymai.ru/eng/published.php?ID=62159
  4. Beniaich A., Silva M.L.N., Avalos F.A.P., Menezes M.D., Candido B.M. Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera, Semina: Ciencias Agrarias, 2019, vol. 40 (1), pp. 49-66. DOI:10.5433/1679-0359.2019v40n1p49
  5. Arroyo J., Guijarro M., Pajares G. An instance-based learning approach for thresholding in crop images under different outdoor conditions, Computers and Electronics in Agriculture, Athens, 2016, vol. 127, pp. 669-679. DOI:10.1016/j.compag.2016.07.018
  6. Dandois J.P., Olana M., Ellis E.C. Optimal altitude, overlap and weather conditions for computer vision UAV estimates of forest structure, Remote Sensing, 2015, vol. 7, no. 10, pp. 13895-13920. DOI:10.3390/rs71013895
  7. Hunt J., Raymond E., Hively W.D., Fujikawa S., Linden D., Daughtry C.S., McCarty G. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring, Remote Sensing, 2010, vol. 2 (1), pp. 290–305. DOI:10.3390/rs2010290
  8. Torres-Sanchez J., Pena J.M., Castro A.I., Lopez-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV, Computers and electronics in agriculture, 2014, vol. 13, pp. 104-113. DOI:10.1016/j.compag.2014.02.009
  9. Wen J., Liu Q., Liu, Q., Xiao Q., Li X. Scale effect and scale correction of land-surface albedo in rugged terrain // International Journal of Remote Sensing, 2009, vol. 30, pp. 5397–5420. DOI:10.1080/01431160903130903
  10. Aboutalebi M., Torres-Rua A. F., McKee M., Nieto H., Kustas W., Coopmans C. The impact of shadows on partitioning of radiometric temperature nto canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T), Proceedings SPIE. Autonomous air and ground sensing systems for agricultural optimization and phenotyping IV, 2019, vol. 11008 (3). DOI:10.1117/12.2519685
  11. Niu H., Zhao T., Wang D., Chen Y. Evapotranspiration Estimation with UAVs in Agriculture: A Review, Conference: 2019, Boston, Massachusetts, July 7- July 10, 2019. DOI:10.13031/aim.201901226
  12. Aboutalebi M., Torres-Rua A. F., McKee M., Nieto H., Kustas W. P., Coopmans C. Validation of digital surface models (DSMs) retrieved from unmanned aerial vehicle (UAV) point clouds using geometrical information from shadows, Proceedings of SPIE. The International Society for Optical Engineering. Autonomous air and ground sensing systems for agricultural optimization and phenotyping III, 2019. DOI:10.1117/12.2519694
  13. Chen Y., Wen D., Jing, L., Shi P. Shadow information recovery in urban areas from very high resolution satellite imagery, International Journal of Remote Sensing, 2007, vol. 28 (15), pp. 3249–3254. DOI:10.1080/01431160600954621
  14. Nieto H., Kustas W.P., Torres-Ra A., Alfieri J.G., Gao F. et al. Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery, Irrigation Science, 2019, vol. 37 (3), pp. 389-406. DOI: 10.1007/s00271-018-0585-9
  15. Qin W., Wang, J., Ma L., Wang F., Hu N. et al. UAV-Based Multi-Temporal Thermal Imaging to Evaluate Wheat Drought Resistance in Different Deficit Irrigation Regimes, Remote Sensing, 2022, vol. 14, pp. 5608. DOI: 10.3390/ rs14215608
  16. Aboutalebi M., Torres-Rua A. F., McKee M., Kustas W., Nieto H., Coopmans C. Behavior of vegetation/soil indices in shaded and sunlit pixels and evaluation of different shadow compensation methods using UAV high-resolution imagery over vineyards, Proceedings of SPIE. The International Society for Optical Engineering. Autonomous air and ground sensing systems for agricultural optimization and phenotyping III, 2018, July 30, pp. 10664. DOI:10.1117/12.2305883
  17. Elarab M., Ticlavilca A.M., Torres-Rua A.F., Maslova I., McKee M. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture, International Journal of Applied Earth Observation and Geoinformation, 2015, vol. 43, pp. 32-42. DOI:10.1016/j.jag.2015.03.017
  18. Sarabandi P., Yamazaki F., Matsuoka M., Kiremidjian A. Shadow detection and radiometric restoration in satellite high resolution images, Conference: Geoscience and Remote Sensing Symposium, IGARSS ’04. 2004, vol. 6. DOI:10.1109/IGARSS.2004.1369936
  19. Victor J.D. Tsai. Automatic Shadow Detection and Radiometric Restoration on Digital Aerial Images, IGARSS 2003, Conference: Geoscience and Remote Sensing Symposium, 2003, vol. 2. DOI:10.1109/IGARSS.2003.1293899
  20. Arévalo V., González J. An experimental evaluation of non-rigid registration techniques on Quickbird satellite imagery // International Journal of Remote Sensing, 2008, vol. 29 (2), pp. 513–527. DOI:10.1080/01431160701241910

Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

 Вход