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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 3, pp. 51-64

Detection of forest disturbances in Sentinel-2 images with convolutional neural networks

A.V. Tarasov 1 , A.N. Shikhov 1 , T.V. Shabalina 1 
1 Perm State University, Perm, Russia
Accepted: 17.06.2021
DOI: 10.21046/2070-7401-2021-18-3-51-64
Monitoring of forest disturbances is an important application field in forest management. Deep learning (convolutional neural network, CNN) is state-of-the-art approach to improve accuracy of forest changes detection. In this study, the algorithm for forest losses detection with Sentinel-2 images based on U-Net architecture was proposed. Training and evaluation were conducted on our own dataset compiled on Sentinel-2 images for several regions of the European Russia. More than 50 experiments with base U-Net architecture to find the best model were performed. It was found that Red, NIR, SWIR1, SWIR2 bands of Sentinel-2 images and their differences were the most important features for forest change detection. General model for all season and separate models for summer, winter and transitional season were developed. Against traditional methods based on map algebra substantial improvement (more than two times) of the accuracy of forest disturbances detection was achieved by the developed models. The developed CNN-based models identified selective loggings as single (unified) forest loss areas as opposed to traditional methods that detect only pixels with strong difference of spectral reflectance. The developed models can be applied in forest change detection in autumn-winter season due to lower shadow sensitivity. Main limitation of the models is time-consuming compilation of training dataset. However, training dataset size increase facilitates adaptation of the algorithm to new data.
Keywords: forest change detection, Sentinel-2 data, convolutional neural networks, deep learning, semantic segmentation, U-Net
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