Polarimetric ISAR Super-Resolution Based on Group Residual Attention Network

Li, MingDian; Cui, Xing-Chao; Yang, ChengLi; Xiao, Shunping; Chen, SiWei

doi:10.1109/icicn56848.2022.10006508

Cited by 1 publication

(1 citation statement)

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“…The HISEA-1 [15] and StarLink [16] satellites are adopted for investigation. The compact polarimetric ISAR echoes are collected using the electromagnetic simulation method [17] under 91 perspectives and the image dataset is constructed using different ISAR image parameters shown in Table 1. In addition, the original images are cropped into with a pixel size of 20 20  and 40 40  for the low-resolution and high-resolution images, respectively.…”

Section: Compact Polarimetric Isar Image and Semantic Information Dat...mentioning

confidence: 99%

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

Li,

Dai,

Deng

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

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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.

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Section: Compact Polarimetric Isar Image and Semantic Information Dat...mentioning

confidence: 99%