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Tree top detection using local maxima filtering: a parameter sensitivity analysis

Jean-Matthieu Monnet 1, * Eric Mermin 1 Jocelyn Chanussot 2 Frédéric Berger 1
* Corresponding author
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
GIPSA-DIS - Département Images et Signal, GIPSA-PSD - GIPSA Pôle Sciences des Données
Abstract : The sensitivity of a treetop detection algorithm is investigated by automated evaluation of detection performance for several parameter combinations. The algorithm consists in digital elevation models computation, morphological filtering, Gaussian smoothing and local maxima extraction and selection. The analysis is performed on three field plots located in the French Alps. One is a Norway spruce stand while the two others are dominated by Silver fir and European beech. Detection rates above 42.9% are achieved with less than 4.1% of false positives. Even though some similarities exist regarding resolution and morphological filtering, optimal settings determined on one plot performed uncertainly on the others. Besides, optimised parameters may depend on both the laser data mainly point density and on the forest structure and species.
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https://hal.archives-ouvertes.fr/hal-00523245
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  • HAL Id : hal-00523245, version 1
  • IRSTEA : PUB00029356

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Jean-Matthieu Monnet, Eric Mermin, Jocelyn Chanussot, Frédéric Berger. Tree top detection using local maxima filtering: a parameter sensitivity analysis. 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems (Silvilaser 2010), Sep 2010, Freiburg, Germany. 9 p. ⟨hal-00523245⟩

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