用分位数回归让卫星估树高模型给出置信度，更好应对复杂地形。
Canopy Tree Height Estimation Using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing
- 在现有模型上加分位数回归头，轻松实现不确定性估计。
- 模型在复杂地形和植被不均区域的预测置信度更低。
- 适合需要评估风险的生态监测与碳储量估算场景。
精确的树高估测对生态监测和生物量评估至关重要。本文将分位数回归应用于基于卫星数据的树高估测模型，引入不确定性量化能力。当前多数方法仅提供点预测，难以满足风险敏感场景需求。通过微调预测头，现有模型可生成统计校准的不确定性估计。同时，实验表明模型在地形复杂、植被异质性高的区域置信度下降，与遥感实际挑战一致，验证了其可靠性。
原文摘要 · Abstract (English)
Accurate tree height estimation is vital for ecological monitoring and biomass assessment. We apply quantile regression to existing tree height estimation models based on satellite data to incorporate uncertainty quantification. Most current approaches for tree height estimation rely on point predictions, which limits their applicability in risk-sensitive scenarios. In this work, we show that, with minor modifications of a given prediction head, existing models can be adapted to provide statistically calibrated uncertainty estimates via quantile regression. Furthermore, we demonstrate how our results correlate with known challenges in remote sensing (e.g., terrain complexity, vegetation heterogeneity), indicating that the model is less confident in more challenging conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。