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

The accurate and rapid extraction of glacier boundaries plays an important role in the study of glacier inventory, glacier change and glacier movement. With the successive launches of high-resolution remote-sensing satellites and the increasing abundance of available remote-sensing data, great opportunities and challenges now exist. 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 Gaofen-6 panchromatic and multispectral (PMS) images with a spatial resolution of 2 m. In the solution, test-time augmentation (TTA) was adopted to increase model robustness, and the convolutional block attention module (CBAM) was added into the atrous spatial pyramid pooling (ASPP) structure in DeepLab V3+ to enhance the weight of the target pixels and reduce the impact of superfluous features. The results show that the improved model effectively increases 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 full convolutional network (FCN), U-Net and DeepLab V3+, the improved model performs better in the test dataset. Moreover, our method achieves superior performance for glacier boundary extraction in parts of the Tanggula Mountains, the Kunlun Mountains and the Qilian Mountains based on Gaofen-6 PMS images. It could distinguish glaciers from terminal moraine lakes, thin snow and clouds, thus demonstrating excellent performance and great potential for rapid and precise extraction of glacier boundaries.

Automated summary

The study proposes an improved model, Attention DeepLab V3+, and a complete solution for automated extraction of glacier boundaries. The results show that the improved model effectively enhances robustness, increases the weight of target pixels, and reduces the influence of non-target elements. Compared with other deep-learning models, the improved model performs better in the test dataset and achieves superior performance in glacier boundary extraction in certain regions.