Articles | Volume 16, issue 10
https://doi.org/10.5194/tc-16-4273-2022
https://doi.org/10.5194/tc-16-4273-2022
Research article
 | 
13 Oct 2022
Research article |  | 13 Oct 2022

Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism

Xinde Chu, Xiaojun Yao, Hongyu Duan, Cong Chen, Jing Li, and Wenlong Pang

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Cited articles

Azam, M. F., Wagnon, P., Berthier, E., Vincent, C., Fujita, K., and Kargel, J. S.: Review of the status and mass changes of Himalayan-Karakoram glaciers, J. Glaciol., 64, 61–74, https://doi.org/10.1017/jog.2017.86, 2018. 
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Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning, Remote Sens.-Basel, 11, 2529, https://doi.org/10.3390/rs11212529, 2019. 
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Short summary
The available remote-sensing data are increasingly abundant, and the efficient and rapid acquisition of glacier boundaries based on these data is currently a frontier issue in glacier research. In this study, we designed a complete solution to automatically extract glacier outlines from the high-resolution images. Compared with other methods, our method achieves the best performance for glacier boundary extraction in parts of the Tanggula Mountains, Kunlun Mountains and Qilian Mountains.