An Extraction Method for Glacial Lakes Based on Landsat-8 Imagery Using an Improved U-Net Network

Remote sensing monitoring of glacial lakes is an indispensable tool for identifying and preventing glacial lake disasters. At present, the existing extraction methods of glacial lakes based on Landsat remote sensing image have achieved remarkable results, but the algorithms used lack the ability to analyze glacial lake spectral and shape and texture features, and require manual design parameters to fine tune the automation of the algorithm. As a result, it cannot mine the depth features of glacier lakes in remote sensing images accurately enough. To address these challenges, this study designed a self-attention mechanism module U-net network that enhances the propagation of features, reduces information loss, strengthens the weight of glacial lake areas, restrains the weight of irrelevant features, reduces the influence of low image contrast on the model, and deals with the variety of pixel categories in glacial lakes. These features improve the performance of the model. Based on Landsat-8 images, we first extracted glacial lakes in large-scale alpine areas using a U-net network model. To make it a self-attention U-net network, we introduced the attention mechanism into the step connection part of the U-net network to adjust feature weight, focus on learning glacial lake features, and strengthen the network to extract the glacial lake features. Finally, we selected the combination of bands 3, 5, and 6 and all bands of Landsat-8 images sing the self-attention U-net network to extract glacial lakes in the study area and compared and analyzed the extraction results. The experimental results and analyses revealed that the proposed method can effectively segment glacial lakes from Landsat-8 remote sensing images. Its effectiveness was proven by different evaluation indices. Compared with a standard U-net network, the true positive for the combination of 3, 5, and 6 bands increased by 15.95% and for all bands by 5.79%. The area under curve for the whole study area reached 85.03% for all bands. The improved U-net network can, thus, meet the real time needs of glacial lake disaster information acquisition.