发表机构
University of California at Los Angeles; NASA Jet Propulsion Laboratory, California Institute of Technology（加州大学洛杉矶分校; 美国国家航空航天局喷气推进实验室，加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估地球预训练表征迁移至土卫六SAR地形分类的效果，比较多种编码器，发现预训练特征优于经典特征，但进一步训练效果因模型而异。
AI 中文摘要
卡西尼号合成孔径雷达（SAR）图像揭示了土卫六的沙丘、平原和湖盆，为利用地球影像学习到的表征进行行星地形分类提供了一个实例。基于SAR的地球到土卫六迁移跨域评估（CETUS）将DINOv2、DOFA和CROMA的特征与经典图像测量以及未训练的视觉变换器特征在美国地质调查局的卡西尼号SAR镶嵌图上进行比较。分类器从专家地貌图中学习地形标签，并在地理上分离的土卫六区域预测这些标签。在逻辑回归设置下，预训练编码器比组合的经典特征获得更高的平均宏F1分数。当特征缩放、优化和正则化同时改变时，编码器的排名会发生变化。在土卫六上的进一步训练提高了DINOv2的性能，降低了DOFA的性能，并在测试设置下导致CROMA的结果好坏参半。架构和输入处理的差异使得这些比较无法隔离预训练的效果。在评估表征迁移用于行星制图时，分类器拟合和单个地形类别的性能很重要。由于地图部分基于相同的雷达观测，分数衡量的是与专家解释的一致性。
英文摘要
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.
CommentsResearch work at NASA Jet Propulsion Laboratory. Available at: https://github.com/magnaprog/CETUS
