Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 27-34, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-27-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 27-34, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-27-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

DETECTION OF CITRUS TREES FROM UAV DSMS

A. O. Ok and A. Ozdarici-Ok A. O. Ok and A. Ozdarici-Ok
  • Dept. of Geodesy and Photogrammetry, Nevsehir H.B.V. University, 50300 Nevsehir, Turkey

Keywords: Citrus Trees, DSM, Radial Symmetry, Local Maxima, Automated Detection, UAVs

Abstract. This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the state-of-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.