Refocusing Moving Ship Targets in SAR Images Based on Fast Minimum Entropy Phase Compensation

Due to self-motion and sea waves, moving ships are typically defocused in synthetic aperture radar (SAR) images. To focus non-cooperative targets, the inverse SAR (ISAR) technique is commonly used with motion compensation. The hybrid SAR/ISAR approach allows a long coherent processing interval (CPI), in which SAR targets are processed with ISAR processing, and exploits the advantages of both SAR and ISAR to generate well-focused images of moving targets. In this paper, based on hybrid SAR/ISAR processing, we propose an improved rank-one phase estimation method (IROPE). By using an iterative two-step convergence approach in the IROPE, the proposed method achieves accurate phase error, maintains robustness to noise and performs well in estimating various phase errors. The performance of the proposed method is analyzed by comparing it with other focusing algorithms in terms of processing simulated data and real complex image data acquired by Gaofen-3 (GF-3) in spotlight mode. The results demonstrate the effectiveness of the proposed method.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid SAR/ISAR Approach for Refocusing Maritime Moving Targets with the GF-3 SAR Satellite

Yan

Zhang

2020

“…The last three papers [17][18][19] of the special issue present SAR imaging algorithms devoted to two sea applications, namely, ship detection and classification, and oceanic eddy detection and analysis.…”

Section: Sea Applicationsmentioning

confidence: 99%

“…The work in [17] presents a new method for refocusing moving ships in SAR images. Ship detection is widely required for both civilian and military surveillance; however, SAR images derived by standard focusing suffer from strong blurring in presence of moving ships.…”

Section: Sea Applicationsmentioning

confidence: 99%

Special Issue “Synthetic Aperture Radar (SAR) Techniques and Applications”

Bovenga

2020

This editorial of the special issue titled “Synthetic Aperture Radar (SAR) Techniques and Applications”, reviews the nineteen papers selected for publication. The proposed studies investigate different aspects of SAR processing including signal modelling, simulation, image analysis, as well as some examples of applications. The papers are grouped according to homogeneous subjects, then objectives and methods are summarised, and the more relevant results are commented.

“…These kinds of methods are used to optimize the cost function, which enables the scene to be well focused. One of the most commonly used cost functions is entropy, and such methods are often called the minimum entropy (ME) method [ 10 , 11 , 12 , 13 , 14 , 15 ]. ME-based methods are not restricted by the assumptions in PGA, so there is no limitation on the bandwidth of estimation.…”

Section: Introductionmentioning

confidence: 99%

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Lee

Jung

Kim

2021

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.