Utilization of the U-Net Convolutional Neural Network and Its Modifications for Segmentation of Tundra Lakes in Satellite Optical Images

Abramova, I. A.; Demchev, D. M.; Kharyutkina, E. V.; Savenkova, E. N. and Sudakow, I. A. (2024). Utilization of the U-Net Convolutional Neural Network and Its Modifications for Segmentation of Tundra Lakes in Satellite Optical Images. Atmospheric and Oceanic Optics, 37(2) pp. 205–210.

DOI: https://doi.org/10.1134/S1024856024700404

Abstract

Tundra lakes are an important indicator of climate change; therefore, the analysis of the dynamics of their size is of particular interest. This paper presents the results of using the U-Net convolutional neural network for tundra lakes segmentation in satellite optical images using Landsat data as an example. The comparative assessment of segmentation accuracy is performed for the original U-Net design and its modifications: U-Net++, Attention U-Net, and R2 U-Net, including with weights derived from a pretrained VGG16 network. The segmentation accuracy is assessed based on the results of manual mapping of tundra lakes in northern Siberia. It is shown that more recent U-Net modifications do not provide a practically significant gain in segmentation accuracy, but increase the computational costs. A configuration based on the classic U-Net gives the best result in most cases (the average Soerens coefficient IoU = 0.88). The technique suggested and the resulting estimates can be used in analysis of modern climate trends.

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