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

Remote Sensing

Siham Tabik¹  Francisco Herrera¹  Domingo Alcaraz-Segura²  Anastasiia Safonova³  Yuriy Maglinets³  Alexey Rubtsov³ 

[1] Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain;Department of Botany, University of Granada, 18071 Granada, Spain;Institute of Space and Information Technologies, Siberian Federal University, 660074 Krasnoyarsk, Russia;

关键词: multi-class classification;    drone;    aerial photography;    Siberian fir;    Siberia;    deep-learning;    convolutional neural networks;    forest health;   

DOI  :  10.3390/rs11060643

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown