Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Lee, Haemin; Jung, Chang-Sik; Kim, Ki-Wan

doi:10.3390/s21072370

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…For each selected chip, the brightest scatterer in its range profile was aligned to the range center. Accordingly, scatterers were windowed, assuming the distribution of weak targets as Gaussian noise [40]. Phase gradient was estimated and utilized to calibrate the phase error repeatedly, subsequently deriving the focused image chip after several iterations.…”

Section: Discussionmentioning

confidence: 99%

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Song,

Kim,

Hwang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Along with vessel detection, vessel recognition in high-resolution SAR images was necessary in order to monitor marine vessels effectively. However, lack of target data and phase defocusing of target from its velocity limited the recognition performance, especially when using detectors based on artificial intelligence. This study accordingly proposed effective vessel recognition in high-resolution ICEYE spotlight SAR images consecutively utilizing (i) vessel detector robust to defocused moving vessels and (ii) mitigation of moving target phase distortion. In order to apply quantitative and qualitative training data enhancement, a target velocity SAR phase refocusing function was developed. The proposed target velocity SAR phase refocusing function generated defocused SLC image with respect to different target azimuth velocity, which can be utilized for both training data augmentation and refocusing of velocity-induced phase distortion. Achievement of stable vessel recognition performance was enabled from (i) robust vessel detection on defocused moving vessels and (ii) well-focused detected vessel targets, both of which were consecutively applied using the proposed target velocity SAR phase refocusing function. Vessel detection results demonstrated robust performance regardless of vessel motion and vessel recognition results significantly improved after phase refocusing, both of which were subject to quantitative and qualitative training data enhancement. Performance of the proposed algorithm was analyzed both in terms of phase focusing and velocity estimation. Refocusing performance outperformed that of conventional state-of-the-art autofocusing algorithm, modified Phase Gradient Autofocusing, while azimuth velocity estimation derived the average offset of 0.68 m/s, which was regarded more accurate than previous azimuth velocity estimators based on single-channel SAR image.

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Section: Discussionmentioning

confidence: 99%

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Song,

Kim,

Hwang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…Different to the phase gradient autofocusing methods that require dominant scattering points in the observed scene, phase retrieval and nonparametric autofocusing based on contrast maximization [25][26][27][28], in the present work, the higher-order phase correction of the SAR signal is performed iteratively by applying entropy as an image quality evaluation function, avoiding measurement of dominant scattering points' phase patterns. The suggested iterative image-focusing algorithm runs at high speed and distinguishes with fast convergence.…”

Section: Discussionmentioning

confidence: 99%

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

Lazarov

2022

Electronics

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Maritime application of synthetic aperture radar (SAR) technology for sea-target surveillance and imaging is considered in this study. A SAR scenario, including the kinematics of a SAR satellite and a ship moving on the sea, along with the geometry of the target, are analytically described. A linear frequency modulation (LFM) waveform is applied for the target’s illumination. Based on the target’s geometry, SAR and target kinematics and the LFM waveform, a SAR signal model is synthesized. It is proven that the process of signal formation is a transformation of the three-dimensional (3D) image into a two-dimensional (2D) signal, whereas the target’s 2D imaging is an inverse transformation of the 2D signal into the target’s 2D image. SAR signal components, linear Fourier terms and higher-order phase terms are analytically derived and discussed in detail. Moreover, it is proven that SAR image reconstruction is a motion-compensation procedure, i.e., it removes all phases induced by first- and higher-order motion. Based on the SAR signal analysis, an illustrative iterative image-reconstruction algorithm is derived. The quality of the imaging is evaluated by an entropy cost function. Simulation experiments are carried out to verify the correctness of the theoretical statements in respect of SAR signal formation and image reconstruction.

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“…Then, center shift these strong scatters along the azimuth direction to obtain a center-shifted image Z. This method assumes that the complex reflectivites, except for the dominant scatters, are distributed as zero-mean Gaussian random noises [41]. To accurately estimate the phase error gradient from these dominant targets, the center-shifted image Z is windowed.…”

Section: Fundamental Backgroundmentioning

confidence: 99%

“…This is because the center-shifting dominant scatter operations can not be effectively parallelized. It is well-known that PGA has fast convergence and a sufficient performance for low-frequency errors, but is not suitable for estimating high-frequency phase error [41]. Meanwhile, MEA requires more iterations and more time to converge, but can obtain a more accurate phase error estimation.…”

Section: Comparison With Existing Autofocus Algorithmsmentioning

confidence: 99%

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

et al. 2021

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Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed.

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Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Cited by 7 publications

References 26 publications

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

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