Super-resolution (SR) for remote sensing imagery often fails under out-of-distribution (OOD) conditions, such as rare geomorphic features captured by diverse sensors, producing visually plausible but physically inaccurate results. We present RareFlow, a physics-aware SR framework designed for OOD robustness. RareFlow's core is a dual-conditioning architecture. A Gated ControlNet preserves fine-grained geometric fidelity from the low-resolution input, while textual prompts provide semantic guidance for synthesizing complex features. To ensure physically sound outputs, we introduce a multifaceted loss function that enforces both spectral and radiometric consistency with sensor properties. Furthermore, the framework quantifies its own predictive uncertainty by employing a stochastic forward pass approach; the resulting output variance directly identifies unfamiliar inputs, mitigating feature hallucination. We validate RareFlow on a new, curated benchmark of multi-sensor satellite imagery. In blind evaluations, geophysical experts rated our model's outputs as approaching the fidelity of ground truth imagery, significantly outperforming state-of-the-art baselines. This qualitative superiority is corroborated by quantitative gains in perceptual metrics, including a nearly 40\% reduction in FID. RareFlow provides a robust framework for high-fidelity synthesis in data-scarce scientific domains and offers a new paradigm for controlled generation under severe domain shift.

翻译：遥感影像超分辨率技术在处理分布外条件时往往失效，例如由不同传感器捕获的稀有地貌特征，导致生成视觉上合理但物理上不准确的结果。本文提出RareFlow，一种专为分布外鲁棒性设计的物理感知超分辨率框架。RareFlow的核心是双条件架构：门控ControlNet保持低分辨率输入的细粒度几何保真度，而文本提示为合成复杂特征提供语义引导。为确保物理合理的输出，我们引入多层面损失函数，强制输出与传感器特性保持光谱和辐射一致性。此外，该框架通过采用随机前向传播方法量化其自身预测不确定性；所得输出方差可直接识别陌生输入，从而缓解特征幻觉。我们在新构建的多传感器卫星影像基准上验证RareFlow。在盲评中，地球物理专家评定本模型输出接近真实影像的保真度，显著优于现有先进基线方法。这种定性优势得到感知指标定量提升的佐证，包括FID降低近40%。RareFlow为数据稀缺科学领域的高保真合成提供了鲁棒框架，并为严重域偏移下的可控生成提供了新范式。