Fast and Robust LRSD-Based SAR/ISAR Imaging and Decomposition

Hamid Reza Hashempour*, Majid Moradikia, Hamed Bastami, Ahmed Abdelhadi, Mojtaba Soltanalian

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

Abstract

The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve 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 requirements in 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 article 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 advantage 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 a 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 compared to the state-of-the-art methods.

Original languageEnglish
Article number5227413
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
ISSN0196-2892
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 1980-2012 IEEE.

Keywords

  • Autofocusing
  • inverse synthetic aperture radar (ISAR)
  • low-rank and sparse decomposition (LRSD)
  • quadratic optimization
  • synthetic aperture radar (SAR)

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