Preprints
https://doi.org/10.5194/tc-2022-61
https://doi.org/10.5194/tc-2022-61
 
11 Apr 2022
11 Apr 2022
Status: a revised version of this preprint was accepted for the journal TC and is expected to appear here in due course.

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

Xinde Chu1, Xiaojun Yao1, Hongyu Duan1, Cong Chen2, Jing Li1, and Wenlong Pang3 Xinde Chu et al.
  • 1College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730070, China
  • 2Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
  • 3Xining Center of Natural Resources Comprehensive Survey, China Geological Survey, Xining, 810000, China

Abstract. Accurate and quick extraction of glacier boundaries plays an important role in studies of glacier inventory, glacier change and glacier movement, and it faces great opportunities and challenges due to the increasing availability of high-resolution remote sensing images with larger data volume and richer texture informations. In this study, we improved the DeepLab V3+ as Attention DeepLab V3+ and designed a complete solution based on the improved network to automatically extract glacier outlines from the Gaofen-6 PMS images with a spatial resolution of 2 m. In the solution, the Test-Time Augmentation (TTA) was adopted to increase model robustness, and the Convolutional Block Attention Module (CBAM) was added into the Atrous Spatial Pyramid Poolin (ASPP) structure in DeepLab V3+ to enhance the weight of the target pixels and reduce the impact of useless features. The results show that the improved model effectively improves the robustness of the model, enhances the weight of target image elements and reduces the influence of non-target elements. Compared with deep learning models such as FCN, U-Net and DeepLab3+, the improved model performs better, with OA and Kappa coefficients of 99.58 % and 0.9915 for the test dataset, respectively. Moreover, our method achieves the highest OA and Kappa of 99.40 % and 0.9846 for glacier boundary extraction in parts of the Tanggula Mountains and Kunlun Mountains based on Gaofen-6 PMS images, showing its excellent performance and great potential.

Xinde Chu et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-61', Anonymous Referee #1, 26 May 2022
    • AC1: 'Reply on RC1', Xinde Chu, 08 Jul 2022
  • RC2: 'Comment on tc-2022-61', Anonymous Referee #2, 07 Jun 2022
    • AC2: 'Reply on RC2', Xinde Chu, 08 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-61', Anonymous Referee #1, 26 May 2022
    • AC1: 'Reply on RC1', Xinde Chu, 08 Jul 2022
  • RC2: 'Comment on tc-2022-61', Anonymous Referee #2, 07 Jun 2022
    • AC2: 'Reply on RC2', Xinde Chu, 08 Jul 2022

Xinde Chu et al.

Xinde Chu et al.

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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 remote sensing research. In this study, we designed a complete solution to automatically extract glacier outlines from the High resolution images. Compared with other method, our our method achieves the best performance for glacier boundary extraction in parts of the Tanggula Mountains and Kunlun Mountains.