arXiv：cs.LG· Kevin Lee·· 4 小时前AI 评分29
CETUS：地球训练的表示迁移到土卫六 Cassini SAR 有多远？
CETUS: How Far Do Representations Trained on Earth Transfer to Cassini SAR of Titan?
AI 导读
NASA 喷气推进实验室的 CETUS 研究比较了 DINOv2、DOFA 和 CROMA 等预训练编码器与美国地质调查局 Cassini SAR 镶嵌图上的经典图像特征，在地球影像上学习的表示能否迁移到土卫六地形分类。
正文
Abstract:Cassini synthetic aperture radar (SAR) images reveal the dunes, plains, and lake basins of Titan, providing an instance of representations learned from Earth imagery for planetary terrain classification. Cross-domain Evaluation of Earth-to-Titan Transfer Using SAR (CETUS) compares features from DINOv2, DOFA and CROMA with classical image measurements and features from an untrained vision transformer on the U.S. Geological Survey's Cassini SAR mosaic. The classifiers learn terrain labels from an expert geomorphological map and predict those labels in geographically separate Titan regions. Under logistic regression settings, pretrained encoders achieve higher mean macro F1 than the combined classical features. Encoder rankings change when feature scaling, optimization, and regularization change together. Further training on Titan improves DINOv2 performance, degrades DOFA performance, and leads to mixed results for CROMA under the tested settings. Architectural and input processing differences prevent these comparisons from isolating the effect of pretraining. Classifier fitting and performance on individual terrain classes matter when assessing representation transfer for planetary mapping. Since the map draws partly on the same radar observations, the scores measure agreement with expert interpretation.
| Comments: | Research work at NASA Jet Propulsion Laboratory. Available at: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2610.07576 [cs.CV] |
| (or arXiv:2610.07576v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07576 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kevin Lee [view email]
[v1]
Tue, 6 Oct 2026 01:11:34 UTC (1,825 KB)
来源：arXiv：cs.LG · arxiv.org