Onboard sensors, such as cameras and thermal sensors, have emerged as effective alternatives to Global Positioning System (GPS) for geo-localization in Unmanned Aerial Vehicle (UAV) navigation. Since GPS can suffer from signal loss and spoofing problems, researchers have explored camera-based techniques such as Visual Geo-localization (VG) using satellite imagery. Additionally, thermal geo-localization (TG) has become crucial for long-range UAV flights in low-illumination environments. This paper proposes a novel thermal geo-localization framework using satellite imagery, which includes multiple domain adaptation methods to address the limited availability of paired thermal and satellite images. The experimental results demonstrate the effectiveness of the proposed approach in achieving reliable thermal geo-localization performance, even in thermal images with indistinct self-similar features. We evaluate our approach on real data collected onboard a UAV. We also release the code and \textit{Boson-nighttime}, a dataset of paired satellite-thermal and unpaired satellite images for thermal geo-localization with satellite imagery. To the best of our knowledge, this work is the first to propose a thermal geo-localization method using satellite imagery in long-range flights.

翻译：机载传感器，如摄像头和热传感器，已成为无人机导航中全球定位系统的有效替代方案。由于GPS可能存在信号丢失和欺骗问题，研究人员探索了基于摄像头的技术，例如利用卫星图像的视觉地理定位。此外，热红外地理定位对于低光照环境下的无人机远距离飞行至关重要。本文提出了一种新颖的基于卫星图像的热红外地理定位框架，该框架包含多种域自适应方法，以解决成对热红外与卫星图像可用性有限的问题。实验结果表明，即使是在具有模糊自相似特征的热红外图像中，所提方法也能实现可靠的热红外地理定位性能。我们使用无人机采集的真实数据评估了该方法。我们还公开了代码和数据集\textit{Boson-nighttime}，该数据集包含成对的卫星-热红外图像和未配对的卫星图像，用于基于卫星图像的热红外地理定位。据我们所知，这是首项提出在远距离飞行中利用卫星图像进行热红外地理定位方法的工作。