为遥感高度与生物量估计提供可信赖的不确定性量化方法
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
- 用哨兵1/2时序数据建模噪声和分布差异带来的不确定性
- 两种方法均在三个任务中超越现有基准，且置信度校准良好
- 适合需要可靠预测的城建、森林与气候决策场景
地球观测回归任务如建筑高度、树冠高度和地上生物量估算在城市规划、森林监测和气候政策中至关重要，准确性和可靠性缺一不可。然而大多数深度学习模型仅输出确定性预测，无法提供像素级可靠性判断。这些任务因地表异质性、目标分布偏斜、传感器噪声及高值信号饱和而具有挑战性，因此不确定性估计对可靠推断至关重要。本文利用长达一年的哨兵1号SAR与哨兵2号MSI时序数据，提出两种互补方法：(i) 高斯不确定性建模，联合预测均值与标准差；(ii) 分位数不确定性，估计10%、50%、90%分位数以捕捉非对称和异方差误差分布。两种模型在10米空间分辨率下评估了三个代表性任务。结果表明，两者均达到或超过确定性基准和现有全球产品，并提供校准良好、可解释且具备操作价值的置信度估计。值得注意的是，两种模型在树冠高度估计上均优于当前10米最先进的不确定性感知模型。代码将开源。
原文摘要 · Abstract (English)
Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical. Yet most deep learning models yield only deterministic predictions, providing no indication of per-pixel reliability. These regression tasks are inherently challenging due to heterogeneous land surfaces, skewed target distributions, sensor noise, and signal saturation at high target values, making uncertainty (UC) estimation essential for reliable inference. We address this gap by modeling aleatoric uncertainty using year-long Sentinel-1 SAR and Sentinel-2 MSI time series, proposing two complementary approaches: (i) Gaussian UC, which jointly predicts mean and standard deviation under a Gaussian assumption, and (ii) Quantile UC, which estimates the 10th, 50th, and 90th quantiles to capture asymmetric and heteroscedastic error distributions. Both models are evaluated on three representative EO regression tasks at 10 m spatial resolution. Results show that both approaches match or surpass deterministic benchmarks and existing global products, while delivering well-calibrated, interpretable, and operationally useful confidence estimates. Notably, both models outperform the current 10 m state-of-the-art uncertainty-aware model for canopy height estimation. Our implementation will be available at: https://github.com/RituYadav92/EO-Regression-Uncertainty-Estimation
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