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- W338847892 abstract "This paper reports an analysis of results from processing return signals from canopy covers using a artificial neural network to find if there is an improvement on detecting tree height and position compared to a more classic local maximum filter approach. The hypothesis taken into consideration is that a neural network permits to insert several useful parameters in the decision process thus making it more “programmable” and apt be used in different forest cover situations. Quad-tree is a method to organize the data to optimize the process done by the neural network. We conclude that results from classic methods and the neural network both give significant results compared to ground-truth measured on the terrain. If the network is implemented with a certain number of trainers there is an improvement compared to the local maximum, but the difference is not statistically significant. Nevertheless further improvements can be foreseen in the future thanks to the intrinsic nature of neural networks to be able to include additional nodes to adapt itself to the final objective – tree recognition." @default.
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- W338847892 date "2008-01-01" @default.
- W338847892 modified "2023-09-26" @default.
- W338847892 title "Neural network and quad-tree approach to extract tree position and height from LiDAR data" @default.
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