Abstract
Data from satellites or aerial vehicles are most of the times unlabelled. Annotating such data accurately is difficult, requires expertise, and is costly in terms of time. Even if Earth Observation (EO) data were correctly labelled, labels might change over time. Learning from unlabelled data within a semi-supervised learning framework for segmentation of aerial images is challenging. In this paper, we develop a new model for semantic segmentation of unlabelled images, the Non-annotated Earth Observation Semantic Segmentation (NEOS) model. NEOS performs domain adaptation as the target domain does not have ground truth semantic segmentation masks. The distribution inconsistencies between the target and source domains are due to differences in acquisition scenes, environment conditions, sensors, and times. Our model aligns the learned representations of the different domains to make them coincide. The evaluation results show that NEOS is successful and outperforms other models for semantic segmentation of unlabelled data.
Abstract (translated)
卫星或无人机收集的数据通常没有标签。准确地注释这些数据具有困难性，需要专业知识，并且在时间上代价昂贵。即使地球观测数据（EO）正确地进行了标签，标签也可能会随着时间的推移而变化。在半监督学习框架中，学习未标记的图像分割任务的模型具有挑战性。在本文中，我们开发了一个名为未标记地球观测语义分割（NEOS）的新模型。NEOS在目标域没有真实语义分割掩膜的情况下进行领域适应。目标和源域之间分布不一致的原因是获取场景、环境条件、传感器和时间的差异。我们的模型将不同领域的学习表示对齐，使它们重叠。评估结果显示，NEOS取得了成功，并且对未标记数据的语义分割优于其他模型。
URL
https://arxiv.org/abs/2404.11299
