Prof. Siwei Chen Profile

Prof. Siwei Chen

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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

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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.

Proceedings Article | 29 December 2023 Paper

Joint estimation of space target attitude and size based on polarimetric ISAR images and modified U-net model

Xing-chao Cui, Yao-wen Fu, Si-wei Chen

Proceedings Volume 12976, 129761C (2023) https://doi.org/10.1117/12.3009152

KEYWORDS: Polarimetry, Solar cells, Image segmentation, 3D projection, Radar, Polarization, Error analysis, Data modeling, 3D image processing, Veins

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Accurate parameter estimation of space target attitude and size with inverse synthetic aperture radar (ISAR) image is a tough task, which plays an important role in analyzing and monitoring space awareness situation. Key point extraction is one of the crucial procedures for parameter estimation. Cross-polarization ISAR data works well in edge structure reservation. Moreover, considering the characteristics of ISAR image, U-net model, which performs well in sample-background-image segmentation, is more suitable for key point extraction. Therefore, a joint estimation method for space target attitude and size is proposed in this work based on polarimetric ISAR images and modified U-net model. The main contribution falls on two parts. Firstly, key point extraction procedure is conducted with modified U-net model, the architecture and loss function of which are modified according to the characteristics of polarimetric ISAR images. Secondly, the attitude and size parameter of space target are jointly estimated based on the extracted key points and ISAR projection relationship. Compared with comparative method, the superiority of the proposed method is validated using simulated data. Quantitatively, the mean estimation error of attitude parameter is 0.88° and that of size parameter is 0.12%.

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