OceanSAR-2提升雷达海面观测的通用特征提取能力。
OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation
- 基于改进自监督学习与动态数据筛选，降低训练成本
- 在风场、波高、冰山检测等任务上表现优异
- 适合海洋遥感研究者用于模型评估与开发
我们提出 OceanSAR-2，是基于合成孔径雷达（SAR）海面观测的第二代基础模型。在前代工作基础上，该模型采用优化的自监督学习（SSL）训练策略与动态数据筛选机制，在提升性能的同时显著降低训练成本。OceanSAR-2 在多个下游任务中展现出强大的迁移能力，包括地球物理模式分类、海面风矢量与有效波高估计、以及冰山检测。我们还发布了标准化基准数据集，为 SAR 海洋模型的系统性评估与持续发展提供支持。
原文摘要 · Abstract (English)
We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation. Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies, which enhances performance while reducing training cost. OceanSAR-2 demonstrates strong transfer performance across downstream tasks, including geophysical pattern classification, ocean surface wind vector and significant wave height estimation, and iceberg detection. We release standardized benchmark datasets, providing a foundation for systematic evaluation and advancement of SAR models for ocean applications.
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