An ML-Based Radial Velocity Estimation Algorithm for Moving Targets in Spaceborne High-Resolution and Wide-Swath SAR Systems

Jin, Tiantian; Qiu, Xiaolan; Hu, Donghui; Ding, Chibiao

doi:10.3390/rs9050404

Cited by 25 publications

(24 citation statements)

References 18 publications

(31 reference statements)

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“…Spaceborne SAR can also be operated over long periods in wide-area and real-time observations. In this context, it became a fundamental system for ship target recognition [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

et al. 2019

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The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.Remote Sens. 2019, 11, 620 2 of 17 recognition [11]. In addition, the conventional constant false alarm rate (CFAR) technique is a typical detection method based on the grayscale feature. However, complicated and cluttered backgrounds severely affect CFAR detection performance [12].In recent years, ship target detection based on deep learning (DL) has been widely studied [13,14], using the typical model of convolutional neural network (CNN) [15]. Liu et al. [16] presented a ship detection method, namely sea-land segmentation-based convolutional neural network (SLS-CNN), which combines a SLS-CNN detector, saliency computation, and corner features. Furthermore, Zhao et al. [17] proposed a spaceborne SAR ship detection algorithm based on low complexity CNN. Some other well-known CNN-based target detection methods include faster region-CNN (Faster R-CNN), you only look once (YOLO) list model, etc. For example, Li et al. [18,19] improved detection performance using Faster R-CNN, to successfully provide a densely connected multi-scale neural network [19]. This method is used to solve multi-scale and multi-scene problems in SAR ship detection. Feature maps are fused by densely connecting different feature map layers, rather than information from single feature maps, which represent top-to-down feature map connections. The R-CNN method is used for target recognition in large scene SAR images [20]. Furthermore, Hamza and Cai used YOLOv2 for ship detection [21], which introduced a multitude of enhancements into the original YOLO model.However, these methods may be no longer effective when ghost replicas exist in an imaged scene. The ghost phenomenon is an intrinsic effect of SAR's ambiguity, both in azimuth and range [22,23]. Range ambiguity occurs when different backscattered echoes-one related to a transmitted pulse and the other due to a previous transmission-temporarily overlap during the receiving operation [24]. On the other hand, azimuth ambiguity is caused by the aliasing of each target's Doppler phase...

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“…Spaceborne SAR can also be operated over long periods in wide-area and real-time observations. In this context, it became a fundamental system for ship target recognition [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

et al. 2019

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“…For the static scenes, unambiguous imaging can be achieved by estimating and compensating the amplitude and phase errors between different channels [5][6][7] and by the reconstruction algorithm, which solves the non-uniform sampling problem [8]. The first spaceborne demonstration experiment using the TerraSAR-X dual receiving antenna mode is elaborated in Reference [9], and the processing algorithms for the GF-3 dual-channel mode imaging are described in References [5] and [10]. Nevertheless, the false targets still exist if there are moving targets and they are treated as stationary ones [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The first spaceborne demonstration experiment using the TerraSAR-X dual receiving antenna mode is elaborated in Reference [9], and the processing algorithms for the GF-3 dual-channel mode imaging are described in References [5] and [10]. Nevertheless, the false targets still exist if there are moving targets and they are treated as stationary ones [10][11][12]. False targets (or named ghost targets) of the moving target must be suppressed, especially for the maritime scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…A velocity estimation algorithm proposed in Reference [26] is based on the analysis of signal subspace and noise subspace, but the method suffers from the limitation of at least one redundant channel, which means the number of RX antennas should be greater than the Doppler ambiguity number. In addition, a novel algorithm for velocity estimation conducted in the echo domain is proposed in Reference [10], which applies the maximum likelihood (ML) method to estimate the cone angle as well as the radial velocity.…”

Section: Introductionmentioning

confidence: 99%

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A Simultaneous Imaging Scheme of Stationary Clutter and Moving Targets for Maritime Scenarios with the First Chinese Dual-Channel Spaceborne SAR Sensor

et al. 2019

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The technique of azimuth multichannel synthetic aperture radar (SAR) system has become a potential solution to the irreconcilable conflict between high-resolution and wide-swath (HRWS) confronted with in a traditional SAR system. Unambiguous imaging, especially for a scene with moving targets, is one of the crucial research topics in the HRWS SAR system. This paper proposes a simultaneous imaging scheme of moving targets and stationary clutter for maritime scenarios. First, the moving target echoes are extracted from the stationary clutter. After that, two methods working in completely different principles are used to estimate the radial velocity of each moving target, and the estimated result is used for phase compensation. After that, the moving target echoes are added back to the stationary scene echo and sent to the reconstruction filter. Lastly, the reconstructed echo can be processed by the classical Chirp Scaling (CS) algorithm. Experiments are carried out using the Chinese GaoFen-3 dual-channel data. The estimated velocities of the moving targets are verified by automatic identification service (AIS) information, and the imaging results show that the false targets are effectively suppressed and the moving targets also return to their correct positions along the azimuth.

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“…In past years, SAR technology was developed for ground moving target indication (GMTI) applications [1][2][3][4]. In addition to the military applications of the SAR moving target data, there is a utilization for civil projects, such as traffic monitoring and traffic guidance systems.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

Rahmanizadeh

Amini

2017

Remote Sensing

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SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse SAR imaging methods for the raw data simulations has achieved good results in the simulation of the static targets, but for the simulation of moving targets these methods have a few shortcomings. In this paper, we propose a method to simulate a speckled scene with moving targets in the hybrid domain. First, the scene is simulated, including speckle, which is statistically in accordance with real SAR image behavior. Then, a reverse imaging algorithm (inverse chirp scaling) was applied on the scene to generate the SAR raw data. The moving target simulation was also done in the time-domain as the next step. Finally, the results from two prior steps were superposed to generate the SAR raw data with moving targets. All steps of the proposed method were evaluated separately. The speckle procedure was evaluated by comparing the speckled SAR image before the simulation and the image of the SAR simulated raw data. The results show similar variations in real and imaginary parts of these data. The correlation between the reflectivity map and the SAR images after the simulation was calculated and the obtained correlation coefficient was about 0.95 for different images. The final data were further analyzed for the displacement of moving targets' positions. The results show similar displacement between moving target SAR raw data with a background and without a background.

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An ML-Based Radial Velocity Estimation Algorithm for Moving Targets in Spaceborne High-Resolution and Wide-Swath SAR Systems

Cited by 25 publications

References 18 publications

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

A Simultaneous Imaging Scheme of Stationary Clutter and Moving Targets for Maritime Scenarios with the First Chinese Dual-Channel Spaceborne SAR Sensor

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

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