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.

翻译：本文研究了从卫星影像中获取语义分割预测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，需要能够产生逐像素估计的可扩展方法。尽管该领域的大多数研究集中于场景理解或医学成像，但本研究专门针对遥感和地球观测应用，对现有方法进行了基准测试。我们的评估聚焦于不确定性度量的实际效用，测试其识别预测错误和受噪声污染输入图像区域的能力。实验在两个遥感数据集PASTIS和ForTy上进行，选择它们是因为其在尺度、地理覆盖范围和标签置信度方面存在差异。我们进行了广泛的评估，涵盖了多种模型（如随机分割网络和集成方法）与若干神经架构及不确定性度量的组合。基于研究结果，我们提出了若干实用性建议。