ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 1, pp. 116-126

Semantic segmentation of damaged fir trees in unmanned aerial vehicle images

I.A. Kerchev 1 , К.А. Maslov 2 , N.G. Markov 2 , O.S. Tokareva 2 
1 Institute of Monitoring of Climatic and Ecological Systems SB RAS, Tomsk, Russia
2 National Research Tomsk Polytechnic University, Tomsk, Russia
Accepted: 09.12.2020
DOI: 10.21046/2070-7401-2021-18-1-116-126
A number of recent studies have shown that convolutional neural networks have found their application in the analysis of satellite images and images of the Earth surface, acquired with the use of unmanned aerial vehicles. Especially useful is their capability to automatically extract image features, such as textures and shapes of objects. This paper addresses the problem of the analysis of the state of fir trees in unmanned aerial vehicle images, provides the description of the research objects and conducts an exploratory analysis of the source data that allowed to build a new model of a convolutional neural network. A new convolutional neural network model, which is based on fully convolutional network U-Net architecture, is proposed. This model is used to do semantic segmentation of unmanned aerial vehicle images of fir forests damaged by Polygraphus proximus. In the paper, the architecture of the proposed neural network model is presented, and the main problems are solved regarding the processes of its training and assessment of the quality of semantic segmentation results obtained using the model. The results have shown a relatively high performance of the model while classifying pixels of the classes «Background», «Living trees», «Recently dead trees» and «Long dead trees». At the end, several ways are proposed to increase the performance of the developed model.
Keywords: Polygraphus proximus, fir trees, unmanned aerial vehicle, semantic image segmentation, convolutional neural network, U-Net.
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