Maritime Surveillance With Undersampled SAR

Wang, Yuying; Zhang, Zhimin; Li, Ning; Hong, Feng; Fan, Huaitao; Wang, Xiangyu

doi:10.1109/lgrs.2017.2717459

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Equations from (7) to (10) show that, restoration the desired SAR image from the degraded one can be uniformly represented as an inverse problem with respect to the transformation function γ . However, the diversity and the spacevariance of the transformation function bring challenges to the conventional model-driven methods and it is hard to solve such problem in a unified way.…”

Section: Proposed Methods a Inverse-problem Modelmentioning

confidence: 99%

“…For comparison, traditional methods, e.g. the SF method [6] and the change detection (CD) method [10], are employed. Meanwhile, the JHK U-net architecture in [22] and a recently proposed residual dense U-net model in [21] (abbreviated as RU-net in the following) are also used for comparison.…”

Section: Log 10mentioning

confidence: 99%

“…For example, matching filtering (MF) methods [4], [5], selective filtering (SF) methods [6]- [8] and the search method [9] cannot work for subNyquist SAR because they are all based on the spectrum aliasing model of the conventional SAR. Method in [10] cannot work for the squint-mode SAR as it utilizes the location change of the artifacts in different sub-frequency bands under broad-sight condition. Methods in [3], [11]- [13] employ compressive sensing (CS) technique to deal with the aliasing in subNyquist SAR.…”

Section: Introductionmentioning

confidence: 99%

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SAR Image Restoration From Spectrum Aliasing by Deep Learning

Liu

Liao

2020

IEEE Access

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The diversity and the space-variance of the spectrum-aliasing effects in SAR bring challenges to the available model-driven restoration methods. In this paper, a hybrid data-driven and model-driven deep learning scheme is innovatively proposed to deal with this problem. In this scheme, the SAR image restoration is realize via a U-shaped deep neural network (DNN). Meanwhile, the model of the SAR echo data is used to generate the training data to learn the weighting parameters of the network. The DNN in this method is designed to suit the SAR spectrum-aliasing application. Due to the general SAR echo mathematical model is employed for training data construction, the spectrum aliasing features in different SAR systems can be accommodated and learned. Hence, the proposed method can work well for various SAR configurations. Simulation results using the real Radarsat-1 data as well as the synthetic data prove the effectiveness as well as the robustness of the proposed method.

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Section: Proposed Methods a Inverse-problem Modelmentioning

confidence: 99%

Section: Log 10mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SAR Image Restoration From Spectrum Aliasing by Deep Learning

Liu

Liao

2020

IEEE Access

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“…In order to remove the azimuth shift and match AIS and VPASS information in accurate position of the corresponding SAR image, shift from (2) was compensated by measuring the factors composing it. Target range velocity was estimated by (3), where indicates platform heading angle and SOG, COG denotes the interpolated SOG and COG.…”

Section: B Input Data Preprocessingmentioning

confidence: 99%

Restoration of Authentic Position of Unidentified Vessels in SAR Imagery: A Deep Learning Based Approach

Song

Kim

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Enhancement of vessel detection performance in SAR images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance however, presentation of authentic position of vessels was essential. Accordingly, this study aimed to propose an algorithm which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: AIS and VPASS information. Two different analyses regarding the vessel detection output utilization were therefore presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via (i) velocity estimator complementing the conventional FrFT velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and (ii) measuring the vessel orientation angle from Radon Transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to CNN-based vessel detection algorithms demonstrating ideal detection performances.

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“…TABLE monitoring of marine vessels was regarded essential for maritime safety, which was often conducted using weather-independent SAR image data [1]. Monitoring of marine vessels was widely applied using single or multiclass vessel detection [2].…”

Section: Introductionmentioning

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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Maritime Surveillance With Undersampled SAR

Cited by 15 publications

References 18 publications

SAR Image Restoration From Spectrum Aliasing by Deep Learning

SAR Image Restoration From Spectrum Aliasing by Deep Learning

Restoration of Authentic Position of Unidentified Vessels in SAR Imagery: A Deep Learning Based Approach

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

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