构建首个高分辨率光学与SAR变化检测数据集，评估大模型在雷达图像上的表现。
SkyCap: Bitemporal VHR Optical-SAR Quartets for Amplitude Change Detection and Foundation-Model Evaluation
- 用光学转雷达标签技术，无需专家标注生成雷达变化标签。
- 光学大模型经预训练后在雷达变化检测上达到F1=45.06的最优结果。
- 发现预处理对模型性能影响极大，光学模型不能直接迁移至雷达任务。
线性基础设施监测需可靠高分辨率数据和定期采集。光学超分辨影像易解读且标注简单，但受云层影响中断采集周期；合成孔径雷达（SAR）可实现全天候采集，但标注困难。本文提出SkyCap，通过档案匹配与配准，构建了由光学SkySat与Capella Space SAR影像组成的双时相高分辨率光学-SAR四元组数据集。利用光学到SAR的标签迁移，无需依赖专业标注即可获得雷达幅度变化检测（ACD）标签。我们对SARATR-X在自建雷达数据上进行持续预训练，并评估由此产生的雷达专用基础模型（FM）及SARATR-X，与光学基础模型在不同预处理下的表现。结果显示，经过分贝+Z-score预处理的光学基础模型MTP(ViT-B+RVSA)取得最佳效果（F1_c = 45.06），优于直接在Capella数据上预训练的专用模型。我们观察到模型性能对预处理与预训练统计对齐高度敏感，且光学变化检测中的模型排序无法直接映射至雷达ACD任务。据我们所知，这是首个针对超高分辨率雷达幅度变化检测的基础模型评估。
原文摘要 · Abstract (English)
Change detection for linear infrastructure monitoring requires reliable high-resolution data and regular acquisition cadence. Optical very-high-resolution (VHR) imagery is interpretable and straightforward to label, but clouds break this cadence. Synthetic Aperture Radar (SAR) enables all-weather acquisitions, yet is difficult to annotate. We introduce SkyCap, a bitemporal VHR optical-SAR dataset constructed by archive matching and co-registration of (optical) SkySat and Capella Space (SAR) scenes. We utilize optical-to-SAR label transfer to obtain SAR amplitude change detection (ACD) labels without requiring SAR-expert annotations. We perform continued pretraining of SARATR-X on our SAR data and benchmark the resulting SAR-specific foundation models (FMs) together with SARATR-X against optical FMs on SkyCap under different preprocessing choices. Among evaluated models, MTP(ViT-B+RVSA), an optical FM, with dB+Z-score preprocessing attains the best result (F1$_c$ = 45.06), outperforming SAR-specific FMs further pretrained directly on Capella data. We observe strong sensitivity to preprocessing alignment with pretraining statistics, and the ranking of optical models on optical change detection does not transfer one-to-one to SAR ACD. To our knowledge, this is the first evaluation of foundation models on VHR SAR ACD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。