Confidence assessments of semantic segmentation algorithms in remote sensing are important. It is a desirable property of models to a priori know if they produce an incorrect output. Evaluations of the confidence assigned to the estimates of models for the task of classification in Earth Observation (EO) are crucial as they can be used to achieve improved semantic segmentation performance and prevent high error rates during inference and deployment. The model we develop, the Confidence Assessments of classification algorithms for Semantic segmentation (CAS) model, performs confidence evaluations at both the segment and pixel levels, and outputs both labels and confidence. The outcome of this work has important applications. The main application is the evaluation of EO Foundation Models on semantic segmentation downstream tasks, in particular land cover classification using satellite Copernicus Sentinel-2 data. The evaluation shows that the proposed model is effective and outperforms other alternative baseline models.

翻译：遥感中语义分割算法的置信度评估具有重要意义。模型若能先验地判断其输出是否正确，将是一个理想特性。对地球观测分类任务中模型估计所分配置信度的评估至关重要，因为这可用于提升语义分割性能，并防止推理和部署过程中的高错误率。我们开发的模型——语义分割分类算法置信度评估模型，在区域和像素级别执行置信度评估，并同时输出标签与置信度。本研究成果具有重要应用价值，主要应用于评估地球观测基础模型在语义分割下游任务中的表现，特别是利用哨兵二号卫星数据进行土地覆盖分类的场景。评估结果表明，所提模型效果显著，性能优于其他基线模型。