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 RGB 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 RGB 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 RGB 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存在信号丢失和欺骗问题，研究者已探索基于摄像头的技术，例如利用卫星RGB影像进行视觉地理定位（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。本文提出一种新颖的热成像地理定位框架，该框架使用卫星RGB影像，并包含多种域适应方法以解决配对热成像与卫星影像的有限可用性。实验结果表明，即使在具有模糊自相似特征的热成像中，所提方法也能实现可靠的热成像地理定位性能。我们基于无人机搭载的实际收集数据评估了该方法。此外，我们公开了代码和\textit{Boson-nighttime}数据集，该数据集包含配对的卫星-热成像及非配对卫星影像，用于卫星影像辅助的热成像地理定位。据我们所知，本工作是首次提出在远距离飞行中利用卫星RGB影像进行热成像地理定位的方法。