Mapping mountain glaciers using an improved U-Net model with cSE

Global warming is melting glaciers. Changes in mountain glaciers have a tremendous impact on human life. Regular identification and extraction of glaciers from satellite images are necessary. However, when studying glaciers, materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification process. Therefore, in this study of glacier extraction, we used an improved U-Net model (a channel-attention U-Net) to map glaciers. The model was trained on Landsat 8 Operational Land Imager (OLI) data and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and was tested on glaciers in the Pamir Plateau. The results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and GlacierNet. The obtained results were fine-tuned by the conditional random field model, effectively reducing background misidentification.

Automated summary

Global warming is causing glaciers to melt, which has a significant impact on human life. Regular identification and extraction of glaciers are essential for studying their changes. However, the surrounding materials of glaciers often have spectral similarities, leading to misclassification during the identification process. In this study, an improved U-Net model called channel-attention U-Net was used to map glaciers. The model was trained on Landsat 8 OLI data and SRTM DEM and tested on glaciers in the Pamir Plateau, showing higher accuracy in identifying glaciers compared to other models. The results were further refined using the conditional random field model to reduce misidentification of background.