Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition
Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition
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DOI：
10.1109/tgrs.2022.3172018
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发表时间：
2022
影响因子：
8.2
通讯作者：
H. Hashempour;Majid Moradikia;Hamed Bastami;Ahmed M Abdelhadi;M. Soltanalian
中科院分区：
文献类型：
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作者：
H. Hashempour;Majid Moradikia;Hamed Bastami;Ahmed M Abdelhadi;M. Soltanalian
在低阶稀疏分解(LRSD)驱动的静止SAR成像的背景下，早期的工作已经在重构-分解过程中显示出显著的改进。然而，当面对由机载平台不稳定引起的平台剩余相位误差(PRPE)时，这两种框架都不能达到令人满意的性能。更重要的是，尽管实时处理要求在遥感应用中具有重要意义，但这些已有的工作只专注于提高成像质量，而没有减少计算负担。针对这两个问题，本文提出了一种快速统一的联合SAR成像框架，该框架通过稳健的LRSD分解和增强图像背景中的主要稀疏目标和低阶特征。特别是，我们的统一算法避免了用于成像的繁琐的大矩阵求逆的任务，并利用约束二次规划的最新进展来处理由于PRPE而施加的单模约束。此外，我们将我们的方法扩展到ISAR自动对焦和成像。具体地说，由于ISAR图像固有的稀疏性，LRSD框架的主要任务是恢复稀疏图像。通过几个基于合成数据和真实数据的实验，验证了该方法在成像质量和计算成本方面的优越性。
The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary SAR imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve a satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirement in the remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this paper presents a fast and unified joint SAR imaging framework where the dominant sparse objects and low-rank features of the image background are decomposed and enhanced through a robust LRSD. In particular, our unified algorithm circumvents the tedious task of computing the inverse of large matrices for image formation and takes advantages of the recent advances in constrained quadratic programming to handle the unimodular constraint imposed due to the PRPE. Furthermore, we extend our approach to ISAR autofocusing and imaging. Specifically, due to the intrinsic sparsity of ISAR images, the LRSD framework is essentially tasked with the recovery of an sparse image. Several experiments based on synthetic and real data are presented to validate the superiority of the proposed method in terms of imaging quality and computational cost as compared to the state-of-the-art methods.