用AI从树叶缝隙重建地表温度，提前发现火灾热点。
Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection
- 结合信号处理与视觉状态空间模型，恢复被植被遮挡的热信号。
- 模拟数据训练下，均方误差降低2-2.5倍；野外实验提升12.8倍。
- 适用于火点、人体等热源检测，对部分遮挡更鲁棒。
我们提出一种方法，通过融合信号处理与机器学习，实现从森林植被中重建地表温度，支持无人机全自动空中监测，用于早期火灾探测。合成孔径（SA）传感虽能减少树冠遮挡，但引入热模糊。为此，我们训练了一个视觉状态空间模型，从模糊数据中恢复部分遮挡土壤与火点的微弱热信号。为解决真实世界训练数据有限问题，我们利用潜在扩散模型、温度增强和程序化热森林建模生成逼真的地表温度模拟数据。在模拟数据集上，本方法相比传统热成像和未校正的SA成像，均方误差（RMSE）降低2-2.5倍；在野外热点实验中，RMSE分别改善12.8倍和2.6倍。该方法还可泛化至其他热信号，如人体热特征，准确捕捉其形态与范围——在简单阈值法失效场景下尤为关键，而传统成像在部分遮挡时表现不佳。
原文摘要 · Abstract (English)
We present a method to reconstruct surface temperatures through forest vegetation by combining signal processing and machine learning, enabling fully automated aerial wildfire monitoring with drones for early fire detection. Synthetic aperture (SA) sensing reduces canopy occlusion but introduces thermal blur. To overcome this, we train a visual state space model to recover subtle thermal signals of partially occluded soil and fire hotspots from blurred data. To address limited real-world training data, we generate realistic surface temperature simulations using a latent diffusion model, temperature augmentation, and procedural thermal forest modeling. On simulated datasets, our method reduces RMSE by 2-2.5 versus conventional thermal and uncorrected SA imaging; in field experiments on hotspots, RMSE improved by 12.8-fold and 2.6-fold, respectively. Our approach also generalizes to other thermal signals, including human signatures, capturing morphology and extent -- critical where simple thresholding fails -- while conventional imaging struggles with partial occlusion.
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