Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning
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...
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Online Access: | https://www.mdpi.com/2072-4292/11/6/643 http://elib.sfu-kras.ru/handle/2311/128709 https://doi.org/10.3390/rs11060643 |
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ftsiberianfuniv:oai:elib.sfu-kras.ru:2311/128709 2023-05-15T18:19:40+02:00 Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning Safonova, A. Tabik, S. Alcaraz-Segura, D. Rubtsov, A. Maglinets, Y. Herrera, F. Институт космических и информационных технологий Кафедра систем искусственного интеллекта 2019-03 https://www.mdpi.com/2072-4292/11/6/643 http://elib.sfu-kras.ru/handle/2311/128709 https://doi.org/10.3390/rs11060643 unknown Remote Sensing Q1 Safonova, A. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning [Текст] / A. Safonova, S. Tabik, D. Alcaraz-Segura, A. Rubtsov, Y. Maglinets, F. Herrera // Remote Sensing. — 2019. — Т. 11 (№ 6). — С. 643 20724292 https://www.mdpi.com/2072-4292/11/6/643 http://elib.sfu-kras.ru/handle/2311/128709 doi:10.3390/rs11060643 multi-class classification drone aerial photography Siberian fir Siberia deep-learning convolutional neural networks forest health 28.23.37 Journal Article Published Journal Article 2019 ftsiberianfuniv https://doi.org/10.3390/rs11060643 2020-01-21T00:52:17Z 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). Article in Journal/Newspaper Sibirica Siberia Siberian Federal University: Archiv Elektronnych SFU Stolby ENVELOPE(129.531,129.531,62.999,62.999) Remote Sensing 11 6 643 |
institution |
Open Polar |
collection |
Siberian Federal University: Archiv Elektronnych SFU |
op_collection_id |
ftsiberianfuniv |
language |
unknown |
topic |
multi-class classification drone aerial photography Siberian fir Siberia deep-learning convolutional neural networks forest health 28.23.37 |
spellingShingle |
multi-class classification drone aerial photography Siberian fir Siberia deep-learning convolutional neural networks forest health 28.23.37 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 |
topic_facet |
multi-class classification drone aerial photography Siberian fir Siberia deep-learning convolutional neural networks forest health 28.23.37 |
description |
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). |
author2 |
Институт космических и информационных технологий Кафедра систем искусственного интеллекта |
format |
Article in Journal/Newspaper |
author |
Safonova, A. Tabik, S. Alcaraz-Segura, D. Rubtsov, A. Maglinets, Y. Herrera, F. |
author_facet |
Safonova, A. Tabik, S. Alcaraz-Segura, D. Rubtsov, A. Maglinets, Y. Herrera, F. |
author_sort |
Safonova, A. |
title |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_short |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_full |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_fullStr |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_full_unstemmed |
Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning |
title_sort |
detection of fir trees (abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning |
publishDate |
2019 |
url |
https://www.mdpi.com/2072-4292/11/6/643 http://elib.sfu-kras.ru/handle/2311/128709 https://doi.org/10.3390/rs11060643 |
long_lat |
ENVELOPE(129.531,129.531,62.999,62.999) |
geographic |
Stolby |
geographic_facet |
Stolby |
genre |
Sibirica Siberia |
genre_facet |
Sibirica Siberia |
op_relation |
Remote Sensing Q1 Safonova, A. Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning [Текст] / A. Safonova, S. Tabik, D. Alcaraz-Segura, A. Rubtsov, Y. Maglinets, F. Herrera // Remote Sensing. — 2019. — Т. 11 (№ 6). — С. 643 20724292 https://www.mdpi.com/2072-4292/11/6/643 http://elib.sfu-kras.ru/handle/2311/128709 doi:10.3390/rs11060643 |
op_doi |
https://doi.org/10.3390/rs11060643 |
container_title |
Remote Sensing |
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11 |
container_issue |
6 |
container_start_page |
643 |
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1766196865268711424 |