用拓扑特征分析冰川雪层图像，预测深度并揭示方法优劣。
Classification of Firn Data via Topological Features
- 结合子层级与距离变换的拓扑特征提取
- 在多种测试场景下表现各异，无最优方法
- 适合关注可解释性与泛化能力的研究者
本文评估了拓扑特征在通用且稳健分类冰川雪层（firn）图像数据中的表现，旨在理解拓扑特征化的优势、局限与权衡。冰川雪层是尚未压缩成冰的粒状积雪层，其内部结构随深度变化，具有显著的拓扑与几何特征，使拓扑数据分析（TDA）成为连接深度与结构的理想工具。研究采用两类拓扑特征：子层级集特征与距离变换特征，结合持久性曲线，从显微CT图像中预测样本深度。一系列具有挑战性的训练-测试场景表明，无单一方法在所有情况下占优，揭示了精度、可解释性与泛化能力之间的复杂权衡。
原文摘要 · Abstract (English)
In this paper we evaluate the performance of topological features for generalizable and robust classification of firn image data, with the broader goal of understanding the advantages, pitfalls, and trade-offs in topological featurization. Firn refers to layers of granular snow within glaciers that haven't been compressed into ice. This compactification process imposes distinct topological and geometric structure on firn that varies with depth within the firn column, making topological data analysis (TDA) a natural choice for understanding the connection between depth and structure. We use two classes of topological features, sublevel set features and distance transform features, together with persistence curves, to predict sample depth from microCT images. A range of challenging training-test scenarios reveals that no one choice of method dominates in all categories, and uncoveres a web of trade-offs between accuracy, interpretability, and generalizability.
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