Topology graph attention mechanism and its application for polarimetric ISAR satellite oriented component recognition

Ming-Dian Li; Shun-Ping Xiao; Si-Wei Chen

doi:10.1117/12.3037100

4 December 2024 Topology graph attention mechanism and its application for polarimetric ISAR satellite oriented component recognition

Ming-Dian Li, Shun-Ping Xiao, Si-Wei Chen

Author Affiliations +

Proceedings Volume 13283, Conference on Spectral Technology and Applications (CSTA 2024); 132833D (2024) https://doi.org/10.1117/12.3037100
Event: Conference on Spectral Technology and Applications (CSTA 2024), 2024, Dalian, China

Abstract

Polarimetric inverse synthetic aperture radar (ISAR) plays an important role in space surveillance. In comparison to the quad-polarization (quad-pol) mode, the compact polarimetric (CP) mode can balance the hardware costs and polarization information. For satellite observation, the recognition of satellite component can offer valuable insights for attitude inversion and the perception of abnormal motion. Comprehending the interrelationships among components across various categories is expected to enhance the recognition performance and refine the spatial position of detection results. In this vein, a topology graph is constructed, and a component-level attention mechanism is introduced. Consequently, a multi-stage recognition framework is employed for feature enhancement. Experimental results indicate that there is a 5.5% improvement for the mean average precision (mAP) index in two scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming-Dian Li, Shun-Ping Xiao, and Si-Wei Chen "Topology graph attention mechanism and its application for polarimetric ISAR satellite oriented component recognition", Proc. SPIE 13283, Conference on Spectral Technology and Applications (CSTA 2024), 132833D (4 December 2024); https://doi.org/10.1117/12.3037100

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KEYWORDS

Satellites

Polarization

Satellite imaging

Target detection

Deep learning

Radar imaging

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