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Remote Sensing
Volume 18
Issue 1
10.3390/rs18010112
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Robust Low-Rank and Spatio–Temporal Regularization Framework for Moving-Vehicle Detection in Satellite Videos

by
Honghu Hua
1,†,
Jun Chen
1,†,
Qian Yin
1,
Yinghui Gao
2,*,
Rixiang Ni
1,
Feiyu Ren
1,
Wei An
1 and
Hui Xu
1
1
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410005, China
2
Academy of Military Science, Beijing 100091, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2026, 18(1), 112; https://doi.org/10.3390/rs18010112 (registering DOI)
Submission received: 24 November 2025 / Revised: 18 December 2025 / Accepted: 22 December 2025 / Published: 28 December 2025
Versions Notes

Abstract

Satellite videos are widely applied for large-scale surveillance. Existing low-rank matrix decomposition-based methods produce promising results under simple and stationary backgrounds. However, these methods suffer a severe performance drop on satellite videos with complex and dynamic backgrounds. To address these challenges, we propose a matrix-based total variation regularized robust PCA (TV-RPCA) approach for moving-vehicle detection. Specifically, our TV-RPCA uses the partial sum of singular values to model the low-rank background. Moreover, a ℓp norm and a spatial–temporal TV regularization are adopted to encourage the spatial–temporal continuity of foregrounds. The optimization of our TV-RPCA is carried out using the augmented Lagrangian multiplier framework combined with the alternating direction minimization approach. Comprehensive experiments conducted on SkySat and Jilin-1 video data verify the effectiveness of the proposed approach.
Keywords: moving object detection; satellite videos; robust principal component analysis (RPCA); anisotropic total variation regularization moving object detection; satellite videos; robust principal component analysis (RPCA); anisotropic total variation regularization

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MDPI and ACS Style

Hua, H.; Chen, J.; Yin, Q.; Gao, Y.; Ni, R.; Ren, F.; An, W.; Xu, H. Robust Low-Rank and Spatio–Temporal Regularization Framework for Moving-Vehicle Detection in Satellite Videos. Remote Sens. 2026, 18, 112. https://doi.org/10.3390/rs18010112

AMA Style

Hua H, Chen J, Yin Q, Gao Y, Ni R, Ren F, An W, Xu H. Robust Low-Rank and Spatio–Temporal Regularization Framework for Moving-Vehicle Detection in Satellite Videos. Remote Sensing. 2026; 18(1):112. https://doi.org/10.3390/rs18010112

Chicago/Turabian Style

Hua, Honghu, Jun Chen, Qian Yin, Yinghui Gao, Rixiang Ni, Feiyu Ren, Wei An, and Hui Xu. 2026. "Robust Low-Rank and Spatio–Temporal Regularization Framework for Moving-Vehicle Detection in Satellite Videos" Remote Sensing 18, no. 1: 112. https://doi.org/10.3390/rs18010112

APA Style

Hua, H., Chen, J., Yin, Q., Gao, Y., Ni, R., Ren, F., An, W., & Xu, H. (2026). Robust Low-Rank and Spatio–Temporal Regularization Framework for Moving-Vehicle Detection in Satellite Videos. Remote Sensing, 18(1), 112. https://doi.org/10.3390/rs18010112

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