评估遥感图像分割模型的不确定性，提升预测可靠性。
Uncertainty evaluation of segmentation models for Earth observation
- 采用多种随机网络与集成方法生成像素级不确定性估计。
- 在两个遥感数据集上验证，能有效识别错误预测区域。
- 为遥感领域提供可落地的不确定性评估实用建议。
本文研究从卫星图像中进行语义分割时的不确定性估计方法。与标准图像分类相比，分割任务的不确定性估计面临独特挑战，需生成可扩展的像素级结果。尽管已有研究多集中于场景理解或医学影像，但本工作专门针对遥感与地球观测应用，对现有方法进行基准测试。评估聚焦于不确定性度量的实际效用，检验其识别预测错误和噪声图像区域的能力。实验在两个遥感数据集PASTIS和ForTy上展开，二者在尺度、地理覆盖范围和标签置信度上存在差异。评估涵盖多种模型（如随机分割网络、集成模型）与神经架构组合，并使用多种不确定性指标。基于结果提出若干实际建议。
原文摘要 · Abstract (English)
This paper investigates methods for estimating uncertainty in semantic segmentation predictions derived from satellite imagery. Estimating uncertainty for segmentation presents unique challenges compared to standard image classification, requiring scalable methods producing per-pixel estimates. While most research on this topic has focused on scene understanding or medical imaging, this work benchmarks existing methods specifically for remote sensing and Earth observation applications. Our evaluation focuses on the practical utility of uncertainty measures, testing their ability to identify prediction errors and noise-corrupted input image regions. Experiments are conducted on two remote sensing datasets, PASTIS and ForTy, selected for their differences in scale, geographic coverage, and label confidence. We perform an extensive evaluation featuring several models, such as Stochastic Segmentation Networks and ensembles, in combination with a number of neural architectures and uncertainty metrics. We make a number of practical recommendations based on our findings.
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