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Volume 10
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10.3390/drones10020098
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Non-Appearance-Based Discrimination of UAVs and Birds in Optical Remote Sensing: Using Kinematic and Time–Frequency Features

by
Yifei Yao
1,2,3,
Jiazhou Geng
1,2,3,
Guiting Chen
1,2,3,
Tao Lei
1,2,3,*,
Lvjiyuan Jiang
1,2,3 and
Yi Cui
1,2,3
1
National Laboratory on Adaptive Optics, Chengdu 610209, China
2
University of Chinese Academy of Sciences, Beijing 101408, China
3
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
*
Author to whom correspondence should be addressed.
Drones 2026, 10(2), 98; https://doi.org/10.3390/drones10020098
Submission received: 18 December 2025 / Revised: 27 January 2026 / Accepted: 27 January 2026 / Published: 29 January 2026
Versions Notes

Abstract

Unmanned aerial vehicles (UAVs) and birds are typical low-altitude small targets in optical remote sensing, often occupying only a few pixels and exhibiting highly similar appearances, which limits the effectiveness of appearance-based discrimination at long distances and low resolutions. To overcome this, we propose a non-appearance-based classification framework using kinematic and time–frequency features. At the trajectory level, kinematic features—including the coefficient of variation of velocity and acceleration, the Spatiotemporal Box-counting Fractal Dimension (SBFD), and the Local Higuchi Fractal Dimension (LHFD)—quantify multi-scale trajectory complexity. At the scale-variation level, time–frequency features, specifically the Time-Frequency Aware Singular Value Entropy (TF-SVE) derived from bounding-box area sequences, capture non-stationary oscillations from bird wing flapping, reflecting behavioral differences from rigid UAV motion. Experiments on a complex real-world dataset show that stacking these features achieves 99.47% classification accuracy, demonstrating a robust, resolution-invariant, and practically effective approach for non-appearance-based recognition of low-altitude targets.
Keywords: low-altitude targets; UAV and bird discrimination; non-appearance-based classification; Kinematic features; fractal dimension; time-frequency analysis; multi-scale motion features; optical remote sensing low-altitude targets; UAV and bird discrimination; non-appearance-based classification; Kinematic features; fractal dimension; time-frequency analysis; multi-scale motion features; optical remote sensing

Share and Cite

MDPI and ACS Style

Yao, Y.; Geng, J.; Chen, G.; Lei, T.; Jiang, L.; Cui, Y. Non-Appearance-Based Discrimination of UAVs and Birds in Optical Remote Sensing: Using Kinematic and Time–Frequency Features. Drones 2026, 10, 98. https://doi.org/10.3390/drones10020098

AMA Style

Yao Y, Geng J, Chen G, Lei T, Jiang L, Cui Y. Non-Appearance-Based Discrimination of UAVs and Birds in Optical Remote Sensing: Using Kinematic and Time–Frequency Features. Drones. 2026; 10(2):98. https://doi.org/10.3390/drones10020098

Chicago/Turabian Style

Yao, Yifei, Jiazhou Geng, Guiting Chen, Tao Lei, Lvjiyuan Jiang, and Yi Cui. 2026. "Non-Appearance-Based Discrimination of UAVs and Birds in Optical Remote Sensing: Using Kinematic and Time–Frequency Features" Drones 10, no. 2: 98. https://doi.org/10.3390/drones10020098

APA Style

Yao, Y., Geng, J., Chen, G., Lei, T., Jiang, L., & Cui, Y. (2026). Non-Appearance-Based Discrimination of UAVs and Birds in Optical Remote Sensing: Using Kinematic and Time–Frequency Features. Drones, 10(2), 98. https://doi.org/10.3390/drones10020098

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