RSPDBench：在物理接地遥感产品退化条件下对地球观测任务视觉基础模型的基准测试
RSPDBench: Benchmarking Vision Foundation Models on Earth Observation Tasks Under Physically Grounded Remote-Sensing Product Degradations
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中文总结 AI 辅助
该研究提出物理接地的遥感产品退化基准RSPDBench，评估视觉基础模型在真实EO产品缺陷下的鲁棒性，发现退化敏感性具有结构性，复合退化可导致高达38个百分点的额外性能下降。
中文摘要 AI 辅助
针对地球观测（EO）任务的视觉基础模型通常在干净的下游基准上进行评估，但实际运行的EO产品在到达模型之前可能已经包含空间、辐射、对齐、噪声和一致性缺陷。现有的鲁棒性评估通常使用通用图像损坏或广泛的域偏移，这些方法无法隔离这些产品级别的失效模式。我们引入了\ extbf{RSPDBench}，一个物理接地的遥感产品退化基准，用于评估视觉基础模型。RSPDBench在审计的原始退化和复合产品链下评估了五个EO数据集、七个基础模型条目和两个监督基线。每个模型都在其干净选择的原生协议下进行评估，鲁棒性通过其自身干净基线的下降来衡量。我们的分析揭示了退化敏感性具有强烈的结构性：分辨率条件和通道分组的编码器保护不同的失效轴，且同一物理缺陷可能对某个模型有害而对另一个模型有益。复合链暴露了孤立退化无法预测的失效，具有模型依赖的放大、饱和或组件主导效应，超出最强组件的额外下降高达38个百分点。这些结果表明，EO鲁棒性不能仅通过干净准确性或通用扰动测试来表征；还必须针对遥感产品在部署中携带的结构性缺陷进行测量。
英文摘要
Vision foundation models targeting Earth observation (EO) tasks are commonly evaluated on clean downstream benchmarks, but operational EO products can already contain spatial, radiometric, alignment, noise, and harmonization defects before reaching the model. Existing robustness evaluations often use generic image corruptions or broad domain shifts, which do not isolate these product-level failure modes. We introduce \textbf{RSPDBench}, a physically grounded \textbf{r}emote-\textbf{s}ensing-\textbf{p}roduct \textbf{d}egradation \textbf{b}enchmark for vision foundation models. RSPDBench evaluates five EO datasets, seven foundation-model entries, and two supervised baselines under audited primitive degradations and compound product chains. Each model is evaluated under its clean-selected native protocol, with robustness measured as the drop from its own clean baseline. Our analysis reveals that degradation sensitivity is strongly structured: resolution-conditioned and channel-grouped encoders protect different failure axes, and the same physical defect can hurt one model while helping another. Compound chains expose failures that isolated degradations do not predict, with model-dependent amplification, saturation, or component dominance, and excess drops up to $38$ percentage points beyond the strongest component. These results show that EO robustness cannot be characterized by clean accuracy or generic perturbation tests alone; it must also be measured against the structured defects that remote-sensing products carry into deployment.
发表机构
- Colorado State University（科罗拉多州立大学）
机构由 AI 辅助整理，请以论文原文为准。
