Prof. Shun-Ping Xiao Profile

Prof. Shun-Ping Xiao

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Proceedings Article | 4 December 2024 Paper

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

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

Proceedings Volume 13283, 132833D (2024) https://doi.org/10.1117/12.3037100

KEYWORDS: Satellites, Polarization, Target detection, Satellite imaging, Radar imaging, Deep learning

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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.

Proceedings Article | 30 April 2024 Paper

Semantic information guided semi-supervised compact ISAR image super-resolution

Ming-Dian Li, Lin-Yu Dai, Jun-Wu Deng, Shun-Ping Xiao, Si-Wei Chen

Proceedings Volume 13157, 1315713 (2024) https://doi.org/10.1117/12.3018370

Read Abstract +

Polarimetric inverse synthetic aperture radar (ISAR), with its ability to operate in all conditions, plays an important role in space surveillance. The compact polarimetric mode balances hardware complexity and polarimetric information, commonly equipped with ISAR systems. However, the generation of high-resolution ISAR images usually requires a large bandwidth and coherent integration angle, which is constrained by the equipment’s physical conditions. At present, supervised learning methods are often used for image super-resolution in computer vision. However, super-resolution performance is often hampered by the occurrence of artifacts and the inadequate consideration of low-frequency information in low-resolution image data. To address these limitations, this work presents a semantic information guided semi-supervised deep-learning method. This framework incorporates implicit neural representation to extract and better utilize information from low-resolution ISAR images. In addition, semantic and super-resolution information are integrated to regulate the training process. Datasets comprising images and semantic information of compact polarimetric ISAR for satellite targets are constructed. The proposed method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using the Peak-Signal-to-Noise (PSNR) metric. Compared with the typical methods, the proposed approach achieves superior super-resolution performance, with a performance improvement of at least 1.394 dB.

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