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.

翻译：我们提出了一种结合信号处理与机器学习，透过森林植被重建地表温度的方法，实现了利用无人机进行全自动化空中野火监测以实现早期火灾检测。合成孔径（SA）感知可减少冠层遮挡，但同时引入了热模糊。为解决此问题，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及火灾热点的微弱热信号。为应对真实训练数据不足，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了逼真的地表温度模拟。在模拟数据集上，与常规热成像及未校正的SA成像相比，我们的方法将RMSE降低了2-2.5；在热点的现场实验中，RMSE分别改善了12.8倍和2.6倍。我们的方法也能泛化至其他热信号（包括人体特征），可捕捉其形态与范围——这在简单阈值法失效时至关重要——而常规成像在部分遮挡情况下表现不佳。