Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests

Yago Diez, Sarah Kentsch, Maximo Caceres, Ha Nguyen, Daniel Serrano, Ferran Roure

Abstract

Counting trees is a common problem in forest applications often solved by performing field studies that are exceedingly cost-intensive in time and manpower. Consequently, many researchers have used computer vision techniques to automatically detect trees by finding tree tops. The success of these algorithms is highly dependent on the data that they are used on. We present a study using data acquired by ourselves in a natural mixed forest using an Unmanned Aerial Vehicle (UAV). Given the particularly challenging nature of our data, we developed a pre-processing step aimed at preparing the data so that it could be used with six common clustering algorithms to detect tree tops. Extensive experiments using data covering over 40 ha is presented and tree detection accuracy, tree counting metrics and computation and use time considerations are taken into account. Our algorithms detect over 80% with high location accuracy and up to 90% with lower accuracy. Tree counting errors range from 8% to 14% for most methods. Data Acquisition and runtime considerations show how this techniques are ready to have an immediate impact in the processing of real forest data.

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Paper Citation


in Harvard Style

Diez Y., Kentsch S., Caceres M., Nguyen H., Serrano D. and Roure F. (2020). Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 75-87. DOI: 10.5220/0009165800750087


in Bibtex Style

@conference{icpram20,
author={Yago Diez and Sarah Kentsch and Maximo Caceres and Ha Nguyen and Daniel Serrano and Ferran Roure},
title={Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={75-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009165800750087},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests
SN - 978-989-758-397-1
AU - Diez Y.
AU - Kentsch S.
AU - Caceres M.
AU - Nguyen H.
AU - Serrano D.
AU - Roure F.
PY - 2020
SP - 75
EP - 87
DO - 10.5220/0009165800750087