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（GPS）的有效替代方案。由于GPS可能面临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（VG）。此外，热成像地理定位（TG）在低光照环境下的远距离无人机飞行中变得至关重要。本文提出了一种新颖的基于卫星图像的热成像地理定位框架，该框架包含多种域适应方法，以解决配对热图像与卫星图像数据有限的难题。实验结果表明，所提方法能够在热图像特征模糊的情况下实现可靠的热成像地理定位性能。我们在无人机载实时数据上评估了该方法，并公开了代码及\textit{Boson-nighttime}数据集——该数据集包含配对的卫星-热图像及非配对的卫星图像，可用于基于卫星图像的热成像地理定位。据我们所知，这是首次在远距离飞行中提出基于卫星图像的热成像地理定位方法。