Abstract
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise due to the labor-intensive and time-consuming nature of pixel-wise annotation, which makes it challenging for human annotators to label every pixel accurately. Annotation errors can significantly degrade the performance and robustness of modern segmentation models, motivating the need for reliable mechanisms to identify and quantify noisy training samples. This paper introduces a novel Data-Centric benchmark, together with a novel, publicly available dataset and two techniques for identifying, quantifying, and ranking training samples according to their level of label noise in remote sensing semantic segmentation. Such proposed methods leverage complementary strategies based on model uncertainty, prediction consistency, and representation analysis, and consistently outperform established baselines across a range of experimental settings. The outcomes of this work are publicly available at this https URL.
Abstract (translated)
高质量的像素级标注对于遥感图像语义分割至关重要。然而，由于逐像素标注工作量大且耗时长，这样的标签获取成本高昂，并且容易受到噪声影响，使得人类标注者难以准确地标记每个像素。注释错误会显著降低现代分割模型的表现和鲁棒性，这促使了对可靠机制的需求来识别和量化带有噪音的训练样本。 本文介绍了一种新的数据为中心的基准测试，以及一个新颖、公开可用的数据集和两种技术，用于根据其标签噪声程度来识别、量化和排序遥感语义分割中的训练样本。这些方法利用基于模型不确定性、预测一致性和表示分析的互补策略，并在各种实验设置中均优于现有基线表现。 该研究的结果可在以下网址获取：[此 HTTPS URL]（请将括号内的文本替换为实际链接地址）。
URL
https://arxiv.org/abs/2603.00604
