ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 75-82, 2016
https://doi.org/10.5194/isprs-annals-III-7-75-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING
Philippe Maillard1 and Marília F. Gomes1,2 1UFMG, Departamento de Geografia, Av. Antˆonio Carlos, 6627, Belo Horizonte - MG, Brazil
2INCRA, Av. Afonso Pena, 3500, Belo Horizonte - MG, Brazil
Keywords: Orchards, VHR Images, Template Matching, Tree Crown Detection, Geometrical-Optical Model, Tree Counting Abstract. This article presents an original algorithm created to detect and count trees in orchards using very high resolution images. The algorithm is based on an adaptation of the “template matching” image processing approach, in which the template is based on a “geometricaloptical” model created from a series of parameters, such as illumination angles, maximum and ambient radiance, and tree size specifications. The algorithm is tested on four images from different regions of the world and different crop types. These images all have < 1 meter spatial resolution and were downloaded from the GoogleEarth application. Results show that the algorithm is very efficient at detecting and counting trees as long as their spectral and spatial characteristics are relatively constant. For walnut, mango and orange trees, the overall accuracy was clearly above 90%. However, the overall success rate for apple trees fell under 75%. It appears that the openness of the apple tree crown is most probably responsible for this poorer result. The algorithm is fully explained with a step-by-step description. At this stage, the algorithm still requires quite a bit of user interaction. The automatic determination of most of the required parameters is under development.
Conference paper (PDF, 7677 KB)


Citation: Maillard, P. and Gomes, M. F.: DETECTION AND COUNTING OF ORCHARD TREES FROM VHR IMAGES USING A GEOMETRICAL-OPTICAL MODEL AND MARKED TEMPLATE MATCHING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 75-82, https://doi.org/10.5194/isprs-annals-III-7-75-2016, 2016.

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