Processing and analysis of satellite videos
DOI:
https://doi.org/10.24867/27BE23RasicKeywords:
Remote sensing, satellite imaging, video signal, tracking of moving objectsAbstract
The paper discusses analysis and processing of video recordings of the Earth's surface made with image sensors placed on satellite platforms. Video sequences made with Jilin-1 satellite which contain significant scene dynamics in the form of moving objects such as vehicles, ships and planes were used. On the example of such recordings, the capabilities of models for tracking moving objects based on the architecture of Siamese neural networks were analyzed. The same object tracking model, without prior adjustment, was also qualitatively tested on local traffic video sequences made with drone and handheld cameras.
References
[1] Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., Gong, P. (2022). An overview of the applications of Earth observation satellite data: impacts and future trends. Remote Sensing, 14(8), 1863.
[2] Zhang, Z, Wang, C., J., & Xu, Y. (2022). Object Tracking based on satellite videos: A literature review. Remote Sensing, 14(15), 3674.
[3] Chen R, Ferreira G, Li X. Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification. Remote Sensing. 2023; 15(1):34.
[4] H. G, T. Blaschke, D. Marceau and A. Bouchard, A comparison of three image object methods for the multiscale analysis of landscape structure., International Journal of Photogrammetry and Remote Sensing, vol. 57, pp. 327-345, 2003.
[5] Li, Y., Jiao, L., Huang, Z., Zhang, X., Zhang, R., Song, X., & Li, L. (2022). Deep learning-based object tracking in satellite videos: A comprehensive survey with a new dataset. IEEE Geoscience and Remote Sensing Magazine, 10(4), 181-212.
[6] Zhang, Z., & Peng, H. (2019). Deeper and wider Siamese networks for real-time visual tracking. CVPR (pp. 4591-4600).
[7] xdai-dlgvv/SV248S, opensource satellite video datasets for object tracking: https://github.com/xdai-dlgvv/SV248S
[8] SiamDW/siamese_tracking: https://github.com/researchmm/SiamDW/tree/master/siamese_tracking
[2] Zhang, Z, Wang, C., J., & Xu, Y. (2022). Object Tracking based on satellite videos: A literature review. Remote Sensing, 14(15), 3674.
[3] Chen R, Ferreira G, Li X. Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification. Remote Sensing. 2023; 15(1):34.
[4] H. G, T. Blaschke, D. Marceau and A. Bouchard, A comparison of three image object methods for the multiscale analysis of landscape structure., International Journal of Photogrammetry and Remote Sensing, vol. 57, pp. 327-345, 2003.
[5] Li, Y., Jiao, L., Huang, Z., Zhang, X., Zhang, R., Song, X., & Li, L. (2022). Deep learning-based object tracking in satellite videos: A comprehensive survey with a new dataset. IEEE Geoscience and Remote Sensing Magazine, 10(4), 181-212.
[6] Zhang, Z., & Peng, H. (2019). Deeper and wider Siamese networks for real-time visual tracking. CVPR (pp. 4591-4600).
[7] xdai-dlgvv/SV248S, opensource satellite video datasets for object tracking: https://github.com/xdai-dlgvv/SV248S
[8] SiamDW/siamese_tracking: https://github.com/researchmm/SiamDW/tree/master/siamese_tracking
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Published
2024-06-06
Issue
Section
Electrotechnical and Computer Engineering
