Advertisement

Estimating Pylon Height Using Differences in Shadows Between GF-2 Images

  • Xiaofei MiEmail author
  • Tao Yu
  • Jian Yang
  • Jibao Lai
  • Zhouwei Zhang
  • Yazhou Zhang
  • Yulin Zhan
Research Article
  • 17 Downloads

Abstract

Information on height of pylon is important when planning the routes of unmanned aerial vehicles. This paper proposes a new automatic method for estimating pylon height using shadow differences between GF-2 satellite images taken at different times. Initially, the spectral distribution features and triangular shape of pylon shadows are used for shadow detection to enhance the difference from other land objects using images taken at a greater sun elevation angle. Then, the difference in the shadows of the same pylon in images taken at different times and shadow-imaging principles are used to estimate the vertical of the pylon top to avoid interference from complex land objects. Finally, the height of the pylon is approximated using the distance from the vertical projection point to the shadow point of the pylon top and sun elevation angle. GF-2 images were selected and included five pylons in the target area. The average error of the estimated height was 1.56 m, and the relative error was only 2.74%.

Keywords

Pylon Shadow Height GF-2 Multi-temporal Vertical projection point 

Notes

Funding

Funding was provided by Major Special Project-the China High-Resolution Earth Observation System (Grant No. 67-Y20A07-9002-16/17).

References

  1. Arévalo, V., González, J., & Ambrosio, G. (2008). Shadow detection in colour high-resolution satellite images. International Journal of Remote Sensing, 29(7), 1945–1963.CrossRefGoogle Scholar
  2. Chen Z, Lan Z, Long H, Hu Q (2014) 3D modeling of pylon from ariborne LiDAR data. Proceedings. SPIE 9158, Remote Sensing of the Enviroment: 18th National Symposium on Remote Sensing of China, 915807  https://doi.org/10.1117/12.2063873.
  3. Cheng, F., & Thiel, K.-H. (1995). Delimiting the building heights in a city from the shadow in a panchromatic SPOT-image—Part 1. Test of forty-two buildings. International Journal of Remote Sensing, 16(3), 409–415.CrossRefGoogle Scholar
  4. Chung, K. L., Lin, Y. R., & Huang, Y. H. (2009). Efficient shadow detection of color aerial images based on successive thresholding scheme. IEEE Transactions on Geoscience and Remote Sensing, 47(2), 671–682.CrossRefGoogle Scholar
  5. Dare, P. M. (2005). Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering & Remote Sensing, 71(2), 169–177.CrossRefGoogle Scholar
  6. Fang, J.-Q., Chen, F., & He, H.-J. (2014). Shadow detection of remote sensing images based on local-classification level set and color feature. Acta Automatica Sinica, 40(6), 134–143.Google Scholar
  7. He, G., Chen, G., He, X. Y., Wang, W., & Liu, D. S. (2011). Extracting buildings distribution information of different heights in a city from the shadows in a panchromatic SPOT image. Journal of Image and Graphics., 6(5), 425–428.Google Scholar
  8. Huertas, A., & Nevatia, R. (1988). Detecting buildings in aerial images. Computer Vision, Graphics, and Image Processing, 41(2), 131–152.CrossRefGoogle Scholar
  9. Li, Q., Chen, Z., & Hu, Q. (2015). A model-driven approach for 3D modeling of pylon from Airborne LiDAR data. Remote Sensing, 7, 11501–11524.CrossRefGoogle Scholar
  10. Liow, Y., & Pavlids, T. (1990). Use of shadows for extracting buildings in aerial images. Computer Vision, Graphics, and Image Processing, 49(2), 242–277.CrossRefGoogle Scholar
  11. Liu, J., Fang, T., & Li, D. (2011). Shadow detection in remotely sensed images based on self-adaptive feature seletion. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 5092–5103.CrossRefGoogle Scholar
  12. Liu, L., Mou, L., & Wang, X. (2010). Model comparison of extracting building height using image shadow. World SCI-TECH R&D., 32(1), 39–42.Google Scholar
  13. Liu, H., & Xie, T. (2013). Study on shadow detection in high resolution remote sensing image based on PCA and HIS mode. Remote Sensing Technology and Application, 28(1), 78–84.Google Scholar
  14. Makarau, A., Richter, R., Muller, R., & Reinartz, P. (2011). Adaptive shadow detection using a blackbody radiator model. IEEE Transactions on Geoscience and Remote Sensing, 4(6), 2049–2059.CrossRefGoogle Scholar
  15. Polidorio, A. M., Flores, F. C., Imai, N. N., Tommaselli, A. M. & Franco, C. (2003). Automatic shadow segmentation in aerial color images. In Computer graphics and image processing, 2003, Brazilian: SIBGRAPI 2003 (pp. 270–277).Google Scholar
  16. Qing, M. Y., Tian, J., & Wang, C. (2013). Detection of pylons in infrared image. Ship Electronic Engineering, 228(6), 125–169.Google Scholar
  17. Salvador, E., Cavallaro, A., & Ebahimi, T. (2004). Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding, 95(2), 238–259.CrossRefGoogle Scholar
  18. Shao, Y., Taff, G. N., & Walsh, S. J. (2011). Shadow detection and building height estimation using IKONOS data. International Journal of Remote Sensing, 32(22), 6929–6944.CrossRefGoogle Scholar
  19. Shettigara, V. K., & Sumerling, G. M. (1998). Height determination of extended objects using shadows in SPOT images. Photogrammetric Engineering & Remote Sensing, 64(1), 35–44.Google Scholar
  20. Tsai, D., & Victor, J. (2006). A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1661–1671.CrossRefGoogle Scholar
  21. Xia, H., & Guo, P. (2011). A shadow detection of remote sensing images based on statistical texture features. Journal of Remote Sensing., 15(4), 778–791.Google Scholar
  22. Yang, J., Zhao, Z., & Yang, J. (2008). A shadow removal method for high resolution remote sensing image. Geomatics and Information Science of Wuhan University, 33(1), 17–20.Google Scholar
  23. Zhang, X. (2016). Research on shadow detection algorithm in high resolution remote sensing image based on multiple features. ChengDu: Southwest Jiaotong University.Google Scholar
  24. Zhang, X., He, G., & Wang, W. (2011). Extracting buildings height and distribution information in Tianjin city from the shadows in ALOS image. Spectroscopy and Spectral Analysis, 31(7), 2003–2006.Google Scholar
  25. Zhou, J., Zhou, Y., & Guo, X. (2011). Methods of extracting distribution information of plants at urban darken areas and repairing their brightness. Journal of East China Normal University (Natural Science), 11(6), 1–9.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.Earth Observation and Data CenterCNSABeijingChina
  3. 3.School of Resources and Environmental EngineeringLudong UniversityYantaiChina
  4. 4.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations