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

Тип публикации: статья из журнала

Год издания: 2019

Идентификатор DOI: 10.3390/rs11060643

Ключевые слова: multi-class classification, drone, aerial photography, Siberian fir, Siberia, deep-learning, convolutional neural networks, forest health

Аннотация: 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).

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Издание

Журнал: REMOTE SENSING

Выпуск журнала: Vol. 11, Is. 6

Номера страниц: 643

ISSN журнала: 20724292

Место издания: BASEL

Издатель: MDPI

Авторы

  • Safonova Anastasia (Siberian Fed Univ, Inst Space & Informat Technol, Krasnoyarsk 660074, Russia; Moscow MV Lomonosov State Univ, Earth Sci Museum, Moscow 119991, Russia; Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain; Univ Granada, Dept Bot, E-18071 Granada, Spain)
  • Tabik Siham (Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain)
  • Alcaraz-Segura Domingo (Univ Granada, Dept Bot, E-18071 Granada, Spain; Univ Almeria, Andalusian Ctr Assessment & Monitoring Global Cha, Almeria 04120, Spain; Univ Granada, Interuniv Inst Earth Syst Res Andalusia IISTA, IEcolab, Granada 18006, Spain)
  • Rubtsov Alexey (Siberian Fed Univ, Inst Space & Informat Technol, Krasnoyarsk 660074, Russia)
  • Maglinets Yuriy (Siberian Fed Univ, Inst Space & Informat Technol, Krasnoyarsk 660074, Russia)
  • Herrera Francisco (Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain)

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