Differences in acquisition time, light conditions, and viewing angle create significant differences among the airborne remote sensing images from Unmanned Aerial Vehicles (UAVs). Real-time scene matching navigation applications based on fixed reference maps are error-prone and have poor robustness. This paper presents a novel shadow-based matching method for the localization of low-altitude flight UAVs. A reference shadow map is generated from an accurate (0.5 m spatial resolution) Digital Surface Model (DSM) with the known date and time information; a robust shadow detection algorithm is employed to detect shadows in aerial images; the shadows can then be used as a stable feature for scene matching navigation. Combining the conventional intensity-based matching method, a fusion scene navigation scheme that is more robust to illumination variations is proposed. Experiments were performed with Google satellite maps, DSM data, and real aerial images of the Zurich region. The radial localization error of the Shadow-based Matching (SbM) is less than 7.3 m at flight height below 1200 m. The fusion navigation approach also achieves an optimal combination of shadow-based matching and intensity-based matching. This study shows the solution to the inconsistencies caused by changes in light, viewing angle, and acquisition time for accurate and effective scene matching navigation.
Wang, H, Cheng, Y, Liu, N, zhao, Y, Chan, JC-W & Li, Z 2022, 'An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight', Remote Sensing, vol. 14, no. 16, 3869. https://doi.org/10.3390/rs14163869
Wang, H., Cheng, Y., Liu, N., zhao, Y., Chan, J. C.-W., & Li, Z. (2022). An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight. Remote Sensing, 14(16), Article 3869. https://doi.org/10.3390/rs14163869
@article{689b9d63dd0e41f6b9ee5cb7193c4b97,
title = "An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight",
abstract = "Differences in acquisition time, light conditions, and viewing angle create significant differences among the airborne remote sensing images from Unmanned Aerial Vehicles (UAVs). Real-time scene matching navigation applications based on fixed reference maps are error-prone and have poor robustness. This paper presents a novel shadow-based matching method for the localization of low-altitude flight UAVs. A reference shadow map is generated from an accurate (0.5 m spatial resolution) Digital Surface Model (DSM) with the known date and time information; a robust shadow detection algorithm is employed to detect shadows in aerial images; the shadows can then be used as a stable feature for scene matching navigation. Combining the conventional intensity-based matching method, a fusion scene navigation scheme that is more robust to illumination variations is proposed. Experiments were performed with Google satellite maps, DSM data, and real aerial images of the Zurich region. The radial localization error of the Shadow-based Matching (SbM) is less than 7.3 m at flight height below 1200 m. The fusion navigation approach also achieves an optimal combination of shadow-based matching and intensity-based matching. This study shows the solution to the inconsistencies caused by changes in light, viewing angle, and acquisition time for accurate and effective scene matching navigation.",
author = "Huaxia Wang and Yongmei Cheng and Nan Liu and yongqiang zhao and Chan, {Jonathan Cheung-Wai} and Zhenwei Li",
note = "Funding Information: This research was funded by the National Natural Science Foundation of China, grant numbers 61771391, 61371152, and 61603364. Publisher Copyright: {\textcopyright} 2022 by the authors.",
year = "2022",
month = aug,
doi = "10.3390/rs14163869",
language = "English",
volume = "14",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Torrent Valencia: Recent Advances, [2024]-",
number = "16",
}