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

• Main Authors: Abramova, I. A., Demchev, D. M., Kharyutkina, E. V., Savenkova, E. N., Sudakow, I. A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: Tundra; Siberia
• Online Access: https://oro.open.ac.uk/98937/; https://oro.open.ac.uk/98937/1/2024_atm_optics_Abramova.pdf

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.