Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. Glaciers in the Hindu Kush Himalaya (HKH) are particularly interesting as the HKH is one of the world's most sensitive regions for climate change. In this work, we: (1) propose a modified version of the U-Net for large-scale, spatially non-overlapping, clean glacial ice, and debris-covered glacial ice segmentation; (2) introduce a novel self-learning boundary-aware loss to improve debris-covered glacial ice segmentation performance; and (3) propose a feature-wise saliency score to understand the contribution of each feature in the multispectral Landsat 7 imagery for glacier mapping. Our results show that the debris-covered glacial ice segmentation model trained using self-learning boundary-aware loss outperformed the model trained using dice loss. Furthermore, we conclude that red, shortwave infrared, and near-infrared bands have the highest contribution toward debris-covered glacial ice segmentation from Landsat 7 images.

翻译：大尺度冰川研究有助于我们理解全球冰川变化，对于监测生态环境、预防灾害以及研究全球气候变化影响至关重要。兴都库什-喜马拉雅（HKH）地区的冰川尤为值得关注，因为该区域是全球气候变化最敏感的地区之一。本研究：（1）提出了一种改进型U-Net架构，用于大尺度、空间非重叠的洁净冰川冰与冰碛覆盖冰川冰分割；（2）引入了一种新型自学习边界感知损失函数，以提升冰碛覆盖冰川冰的分割性能；（3）提出了特征级显著性评分方法，用以评估多光谱Landsat 7影像中各特征对冰川制图的贡献度。结果表明，采用自学习边界感知损失训练的冰碛覆盖冰川冰分割模型性能优于使用Dice损失训练的模型。此外，我们发现红色波段、短波红外波段和近红外波段对Landsat 7影像中冰碛覆盖冰川冰分割的贡献度最高。