Semi-Supervised Semantic Segmentation of Radar Sounder Data With Scribble Annotations

Automatic semantic segmentation of radar sounder data is a critical task for analyzing subsurface structures in planetary and terrestrial studies. In this context, one of the major issues for the development of deep learning models for semantic segmentation is the lack of labeled data, which affects the training of neural architectures. In this study, we propose a semi-supervised learning method that leverages scribble annotations, including points, diagonal lines, and polygons, alongside pseudo-labels generated from unlabeled data. To ensure the reliability of pseudo-labels, we propose selecting them based on confidence and spatial proximity constraints. To validate our method, we performed extensive experiments on terrestrial and planetary datasets. The results demonstrate that our approach consistently outperforms existing semi-supervised methods when trained on scribble annotations. In particular, the proposed method trained on diagonal scribbles yields the highest overall accuracy of 99.4% on the terrestrial dataset and 94.7% on the planetary dataset. These findings indicate that the proposed method achieves performance comparable to those of the fully supervised methods trained on dense annotations, while significantly reducing labeling costs.