近半个世纪以来，受全球气候暖湿化影响，中国冰川消融速度逐渐加剧，加强对冰川分布状况的监测日益重要，如何快速且准确地提取冰川信息，对实现冰川常态化监测工作尤为关键。针对目前冰川自动化提取方法存在的不足，采用面向对象自动分类技术，以青海省门源县为试验区，利用Sentinel-2影像与DEM数据，分析总结了冰川在多维度特征下的表现规律，并针对冰川提取中存在的边界破碎、山体阴影处的漏提、云误提等现象，利用决策树C5.0模型构建了多特征组合的冰川提取规则集与邻域特征的提取规则集，解决了单阶段面向对象自动化提取算法存在的主要问题，形成了基于面向对象的两阶段冰川提取方法，实现了冰川信息的高效自动化提取。试验结果表明：区内冰川在光谱均值、光谱标准差、归一化雪盖指数NDSI、DEM、邻接度、坡向等特征下具有响应规律，构建的两阶段提取方法能够较好地实现冰川的自动化提取，并在云盖区域和山体阴影区域内具有良好的识别效果，识别精度达98.50%。

Over the past half-century, global warming and humidification have led to an accelerated rate of glacier melting in China, highlighting the increasing importance of monitoring glacier distribution. However, current automated glacier extraction methods have significant limitations, such as boundary fragmentation, omission of glaciers in shaded mountain areas, and misclassification in cloud-covered regions. To address these issues, this study selected Menyuan County in Qinghai Province as the experimental area. Sentinel-2 imagery and DEM data were utilized, applying object-oriented automatic classification technology in combination with the C5.0 decision tree model to develop a multi-feature glacier extraction rule set and a neighborhood feature rule set. An improved two-stage object-oriented glacier extraction method was subsequently proposed. The findings revealed that glaciers exhibited distinct response patterns across various features, including spectral mean, spectral standard deviation, NDSI (normalized difference snow index), DEM (digital elevation model), adjacency, and slope orientation. A two-stage glacier extraction method effectively enabled automatic glacier extraction. It also significantly enhanced the recognition accuracy in cloud-covered and shaded mountain regions, achieving an overall glacier recognition accuracy of 98.50%.