Accurate geo-localization of Unmanned Aerial Vehicles (UAVs) is crucial for a
variety of outdoor applications including search and rescue operations, power
line inspections, and environmental monitoring. The vulnerability of Global
Navigation Satellite Systems (GNSS) signals to interference and spoofing
necessitates the development of additional robust localization methods for
autonomous navigation. Visual Geo-localization (VG), leveraging onboard cameras
and reference satellite maps, offers a promising solution for absolute
localization. Specifically, Thermal Geo-localization (TG), which relies on
image-based matching between thermal imagery with satellite databases, stands
out by utilizing infrared cameras for effective night-time localization.
However, the efficiency and effectiveness of current TG approaches, are
hindered by dense sampling on satellite maps and geometric noises in thermal
query images. To overcome these challenges, in this paper, we introduce STHN, a
novel UAV thermal geo-localization approach that employs a coarse-to-fine deep
homography estimation method. This method attains reliable thermal
geo-localization within a 512-meter radius of the UAV's last known location
even with a challenging 11% overlap between satellite and thermal images,
despite the presence of indistinct textures in thermal imagery and self-similar
patterns in both spectra. Our research significantly enhances UAV thermal
geo-localization performance and robustness against the impacts of geometric
noises under low-visibility conditions in the wild. The code will be made
publicly available.

STHN 是一种新的无人机热定位方法，通过粗糙到精细的深度单应性估计方法，在低能见度条件下可可在无人机上次已知位置的 512 米范围内实现可靠的热定位，克服了热影像模糊纹理和自相似模式的困扰，显著提高了无人机热定位性能和鲁棒性。

STHN: 无人机热红外图像与卫星影像的深度齐次估计与地理定位

STHN: Deep Homography Estimation for UAV Thermal Geo-localization with  Satellite Imagery

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.

本文提出了一种利用卫星图像进行长距离无人机热成像定位的新方法，并通过多种域自适应方法解决配对热成像和卫星图像有限的可用性，实验结果表明该方法的有效性，本研究是首次提出在长距离飞行中利用卫星图像进行热成像定位的方法。