Tree Crown Detection, Delineation and Counting in UAV Remote Sensed Images: A Neural Network Based Spectral–Spatial Method

  • Ramesh Kestur
  • Akanksha Angural
  • Bazila Bashir
  • S. N. OmkarEmail author
  • Gautham Anand
  • M. B. Meenavathi
Research Article


UAVs are fast emerging as a remote sensing platform to complement satellite based remote sensing. Agriculture and ecology is one of the important applications of UAV remote sensing, also known as low altitude remote sensing (LARS). This work demonstrates the use and potential of LARS in agriculture, particularly small holder open field agriculture. Two UAVs are used for remote sensing. The first UAV is a fixed wing aircraft with a high spatial resolution visible spectrum also known as RGB camera as a payload. The second UAV is a quadrotor UAV with an RGB camera interfaced to an on-board single board computer as the payload. LARS was carried out to acquire aerial high spatial resolution RGB images of different farms. Spectral–spatial classification of high spatial resolution RGB images for detection, delineation and counting of tree crowns in the image is presented. Supervised classification is carried out using extreme learning machine (ELM), a single hidden layer feed forward network neural network classifier. ELM was modelled for RGB values as input feature vectors and binary (tree and non-tree pixels) output class. Due to similarities in spectral intensities, some of the non-tree pixels were classified as tree pixels and in order to remove them, spatial classification was performed on the image. Spatial classification was carried out using thresholded geometrical property filtering techniques. Threshold values chosen for carrying out spatial classification were analysed to obtain optimal values. Finally in the delineation and counting, the connected tree crowns were segmented using Watershed algorithm performed on the image after marking individual tree crowns using Distance Transform method. Five representative UAV images captured at different altitudes with different crowns of banana plant, mango trees and coconut trees were used to demonstrate the performance of the proposed method. The performance was compared with the traditional KMeans spectral–spatial method of clustering. Results and comparison of performance parameters of KMeans spectral–spatial and ELM spectral–spatial classification methods are presented. Results indicate that ELM performed better than KMeans.


Unmanned aerial vehicle (UAV) Low altitude remote sensing (LARS) Single board computer (SBC) Extreme learning machine (ELM) Single hidden layer feed forward neural network (SLFN) 


  1. Alonzo, M., Bookhagen, B., & Roberts, D. A. (2014). Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment, 148, 70–83.CrossRefGoogle Scholar
  2. Bai, Y., Walsworth, N., Roddan, B., Hill, D. A., Broersma, K., & Thompson, D. (2005). Quantifying tree cover in the forest–grassland ecotone of British Columbia using crown delineation and pattern detection. Forest Ecology and Management, 212(1), 92–100.CrossRefGoogle Scholar
  3. Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., González-Dugo, V., & Fereres, E. (2009). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(6).Google Scholar
  4. Chitade, A. Z., & Katiyar, D. S. (2010). Colour based image classification using KMeans clustering. International Journal of Engineering Science and Technology, 1(2), 5319–5325.Google Scholar
  5. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.CrossRefGoogle Scholar
  6. Holmgren, J., Persson, Å., & Söderman, U. (2008). Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. International Journal of Remote Sensing, 29(5), 1537–1552.CrossRefGoogle Scholar
  7. Huang, G. B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2(2), 107–122.CrossRefGoogle Scholar
  8. Hung, C., Bryson, M., & Sukkarieh, S. (2012). Multi-class predictive template for tree crown detection. ISPRS journal of photogrammetry and remote sensing, 68, 170–183.CrossRefGoogle Scholar
  9. Jing, L., Hu, B., Noland, T., & Li, J. (2012). An individual tree crown delineation method based on multi-scale classification of imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 88–98.CrossRefGoogle Scholar
  10. Kang, J., Wang, L., Jia, K., Niu, Z., Shakir, M., Qiao, H., et al. (2016). Identifying crown areas in an undulating area planted with eucalyptus using unmanned aerial vehicle near-infrared imagery. Remote Sensing Letters, 7(6), 561–570.CrossRefGoogle Scholar
  11. Laliberte, A. S., Herrick, J. E., Rango, A., & Winters, C. (2010). Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering & Remote Sensing, 76(6), 661–672.CrossRefGoogle Scholar
  12. Lucas, R., Bunting, P., Paterson, M., & Chisholm, L. (2008). Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment, 112(5), 2088–2103.CrossRefGoogle Scholar
  13. Malek, S., Bazi, Y., Alajlan, N., AlHichri, H., & Melgani, F. (2014). Efficient framework for palm tree detection in UAV images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4692–4703.CrossRefGoogle Scholar
  14. Man, Q., Dong, P., Guo, H., Liu, G., & Shi, R. (2014). Light detection and ranging and hyperspectral data for estimation of forest biomass: A review. Journal of Applied Remote Sensing, 8(1), 081598.CrossRefGoogle Scholar
  15. Nebiker, S., Annen, A., Scherrer, M., & Oesch, D. (2008). A light-weight multispectral sensor for micro UAV—Opportunities for very high resolution airborne remote sensing. The international archives of the photogrammetry, remote sensing and spatial information sciences, 37(B1), 1193–1199.Google Scholar
  16. Saberioon, M. M., Amin, M. S. M., Anuar, A. R., Gholizadeh, A., Wayayok, A., & Khairunniza-Bejo, S. (2014). Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, 32, 35–45.CrossRefGoogle Scholar
  17. Selim, S. Z., & Ismail, M. A. (1984). KMeans-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1, 81–87.CrossRefGoogle Scholar
  18. Senthilnath, J., Dokania, A., Kandukuri, M., Ramesh, K. N., Anand, G., & Omkar, S. N. (2016). Detection of tomatoes using spectral–spatial methods in remotely sensed RGB images captured by UAV. Biosystems Engineering, 146, 16–32.CrossRefGoogle Scholar
  19. Shi, Y., Murray, S. C., Rooney, W. L., Valasek, J., Olsenholler, J., Pugh, N. A., & Thomasson, J. A. (2016). Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system. In Autonomous air and ground sensing systems for agricultural optimization and phenotyping (vol 9866, p. 98660E). International Society for Optics and Photonics.Google Scholar
  20. Song, C., Dickinson, M. B., Su, L., Zhang, S., & Yaussey, D. (2010). Estimating average tree crown size using spatial information from Ikonos and QuickBird images: Across-sensor and across-site comparisons. Remote Sensing of Environment, 114(5), 1099–1107.CrossRefGoogle Scholar
  21. Suresh, S., Babu, R. V., & Kim, H. J. (2009). No-reference image quality assessment using modified extreme learning machine classifier. Applied Soft Computing, 9(2), 541–552.CrossRefGoogle Scholar
  22. Swain, K. C., Thomson, S. J., & Jayasuriya, H. P. (2010). Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Transactions of the ASABE, 53(1), 21–27.CrossRefGoogle Scholar
  23. Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S. F., Gioli, B., Matese, A., et al. (2017). Forestry applications of UAVs in Europe: A review. International Journal of Remote Sensing, 38(8–10), 2427–2447.CrossRefGoogle Scholar
  24. Turner, D., Lucieer, A., & Watson, C. (2011). Development of an unmanned aerial vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. In Proceedings of 34th International symposium on remote sensing of environment (p. 4).Google Scholar
  25. Zarco-Tejada, P. J., Diaz-Varela, R., Angileri, V., & Loudjani, P. (2014). Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55, 89–99.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringBITBangaloreIndia
  2. 2.Department of Electronics and Communication EngineeringNITSrinagarIndia
  3. 3.Department of Computer Science EngineeringNITSrinagarIndia
  4. 4.Department of Aerospace EngineeringIndian Institute of ScienceBengaluruIndia

Personalised recommendations