SEIS-Net:基于斯温变压器的三维合成孔径雷达增强成像网络

IF 4.7 2区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yifei Hu;Mou Wang;Shunjun Wei;Jiahui Li;Rong Shen
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引用次数: 0

摘要

传统的三维合成孔径雷达(SAR)稀疏成像算法由于依赖于固有的稀疏性,在弱稀疏场景中性能下降。此外,它们还受到高计算复杂度和参数调整的限制。为了解决这些问题,我们提出了一种基于swin变换器的新型三维合成孔径雷达增强成像网络,称为SEIS-Net。所提出的算法由两个级联阶段组成。第一阶段的重点是通过构建基于swin变换器的Unet来估计缺失的测量元素。第二阶段旨在从估计的回波矩阵中恢复高质量图像。从理论上讲,所提出的成像网络来自快速迭代收缩-阈值算法优化框架,网络权重可通过端到端训练程序学习。最后,还进行了模拟和实际测量实验。直观和定量结果都证明,在从稀疏采样回波重建三维图像方面,所提出的 SEIS-Net 优于目前最先进的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SEIS-Net: A 3-D SAR Enhanced Imaging Network Based on Swin Transformer
Conventional 3-D synthetic aperture radar (SAR) sparse imaging algorithms suffer from degradation in weakly sparse scenes due to their reliance on inherent sparsity. In addition, they are constrained by high computational complexity and parametric tuning. To address these problems, we propose a novel 3-D SAR enhanced imaging network based on swin transformer dubbed SEIS-Net. The proposed algorithm consists of two cascaded stages. The first one focuses on estimating the missing measurement elements by constructing a Unet based on the swin transformer. The second stage aims to recover a high-quality image from the estimated echo matrix. The proposed imaging network is theoretically derived from fast iterative shrinkage-thresholding algorithm optimization framework, where the network weights can be learned from an end-to-end training procedure. Finally, simulations and real-measured experiments are carried out. Both visual and quantitative results demonstrate the superiority of the proposed SEIS-Net over the current state-of-the-art algorithms in reconstructing 3-D images from sparsely sampled echoes.
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来源期刊
CiteScore
9.30
自引率
10.90%
发文量
563
审稿时长
4.7 months
期刊介绍: The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.
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