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Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

1
Institute of Space and Information Technologies, Siberian Federal University, 660074 Krasnoyarsk, Russia
2
The Earth Science Museum of M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
3
Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain
4
Department of Botany, University of Granada, 18071 Granada, Spain
5
Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120 Almería, Spain
6
iEcolab., Interuniversity Institute for Earth System Research in Andalusia (IISTA), University of Granada, 18006 Granada, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 643; https://doi.org/10.3390/rs11060643
Received: 4 February 2019 / Revised: 4 March 2019 / Accepted: 12 March 2019 / Published: 16 March 2019
(This article belongs to the Special Issue Convolutional Neural Networks Applications in Remote Sensing)
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Abstract

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia). View Full-Text
Keywords: multi-class classification; drone; aerial photography; Siberian fir; Siberia; deep-learning; convolutional neural networks; forest health multi-class classification; drone; aerial photography; Siberian fir; Siberia; deep-learning; convolutional neural networks; forest health
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Safonova, A.; Tabik, S.; Alcaraz-Segura, D.; Rubtsov, A.; Maglinets, Y.; Herrera, F. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens. 2019, 11, 643.

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