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.

中文翻译：

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基于Landsat-8影像的冰川湖泊提取方法，使用改进的U-Net网络

冰川湖遥感监测是识别和预防冰川湖灾害不可缺少的工具。目前，现有的基于 Landsat 遥感影像的冰川湖泊提取方法取得了显着的效果，但所采用的算法缺乏对冰川湖泊光谱和形状纹理特征的分析能力，需要人工设计参数对提取的自动化进行微调。算法。因此，它不能足够准确地挖掘遥感图像中冰川湖泊的深度特征。为了应对这些挑战，本研究设计了一个自注意力机制模块 U-net 网络，它增强了特征的传播，减少了信息丢失，加强了冰川湖区的权重，抑制了无关特征的权重，减少低图像对比度对模型的影响，并处理冰川湖中各种像素类别。这些特性提高了模型的性能。基于 Landsat-8 图像，我们首先使用 U-net 网络模型提取了大规模高山地区的冰川湖。为了使其成为自注意力的U-net网络，我们在U-net网络的阶梯连接部分引入了注意力机制来调整特征权重，重点学习冰湖特征，加强网络提取冰湖特征. 最后，我们选择3、5、6波段和Landsat-8全波段影像的组合，利用自注意力U-net网络对研究区冰川湖进行提取，并对提取结果进行对比分析。实验结果和分析表明，该方法可以有效地从 Landsat-8 遥感图像中分割冰川湖泊。通过不同的评价指标证明了其有效性。与标准的 U-net 网络相比，3、5、6 频段组合的真阳性增加了 15.95%，所有频段的真阳性增加了 5.79%。整个研究区的曲线下面积达到了所有波段的 85.03%。改进后的U-net网络可以满足冰川湖灾害信息实时获取的需要。整个研究区的曲线下面积达到了所有波段的 85.03%。改进后的U-net网络可以满足冰川湖灾害信息实时获取的需要。整个研究区的曲线下面积达到了所有波段的 85.03%。改进后的U-net网络可以满足冰川湖灾害信息实时获取的需要。