Real-Time Processing of Spaceborne SAR Data With Nonlinear Trajectory Based on Variable PRF

Chen, Jianlai; Zhang, Junchao; Jin, Yanghao; Yu, Hanwen; Liang, Buge; Yang, Dongkai

doi:10.1109/tgrs.2021.3067945

Cited by 69 publications

(14 citation statements)

References 32 publications

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“…The synthetic SAR images are obtained by multiplying optical images by simulated white speckle in amplitude format, which is as same as that used in [12], and the ENL are set 1, 4, and 16. Real-world SAR data (OurSAR) are captured by sensors mounted on an airplane (3GHz, sliding spotlight mode, X band, resolution: 0.05 m × 0.05 m), and images are obtained by using the autofocus method in [35]- [37]. Besides, the AIRSAR image (taken over Flevoland in Netherlands, used in [22]) and TerraSAR image (X band, HH polarization, resolution: 5 m × 5 m, used in [18]) are also adopted to evaluate the performance.…”

Section: Resultsmentioning

confidence: 99%

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Zhang

Chen

et al. 2021

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

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Synthetic aperture radar (SAR) images are inherently degraded by the speckle noise due to the coherent imaging, which may affect the performance of subsequent image analysis task. To address this problem, a weighted sparse representation-based method is proposed in this article for SAR image despeckling. The homomorphic transformation is first adopted to convert multiplicative noise into additive one. Second, similar patches are grouped together to learn the adaptive dictionaries and sparse coefficients based on nonlocal self-similarity constraint. Moreover, weighted regularizations are adopted for coefficients to boost the performance. Finally, despeckling images are obtained via exponential transformation. Experimental results on synthetic and real-world SAR images demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

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Section: Resultsmentioning

confidence: 99%

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Zhang

Chen

et al. 2021

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

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“…Synthetic aperture radar (SAR) is an active earth observation system with high resolution. The scene imaging quality is considerable with optical images; these images are increasingly important for scientific applications [1][2][3][4]. With the increasing improvement of SAR data collection capability and imaging algorithms, the research on interpreting high-resolution SAR images has received extensive attention, such as target detection and change detection [5,6].…”

Section: Introductionmentioning

confidence: 99%

SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network

Fang

Gui

et al. 2022

Remote Sensing

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A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.

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“…In addition, it should be noted that the nonlinear trajectory of monostatic SAR can also cause two-dimensional space-variance characteristics in radar echo signal [9]. In summary, there are two main types of frequency-domain imaging algorithms for the problem of two-dimensional space-variance: i) Scaling-based algorithms: This kind of algorithm corrects the space-variance characteristics of signal by the principle of scaling [10][11][12][13][14]. They can be used not only to correct the range space-variance, but also correct the azimuth space-variance of the signal.…”

Section: Introductionmentioning

confidence: 99%

Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm

Xiong

Liang

et al. 2021

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

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The nonlinear trajectory and bistatic characteristics of general bistatic synthetic aperture radar (SAR) can cause severe two-dimensional space-variance in the echo signal, and therefore it is difficult to focus the echo signal directly using the traditional frequency-domain imaging algorithm based on the assumption of azimuth translational invariance. At present, the state-of-the-art nonlinear trajectory imaging algorithm is based on singular value decomposition (SVD), which has the problem that SVD may be not controlled, and thus may lead to a high imaging complexity or low imaging accuracy. Therefore, this article proposes a nonlinear trajectory SAR imaging algorithm based on controlled SVD (CSVD). Firstly, the chirp scaling algorithm (CSA) is used to correct the range sapce-variance, and then SVD is used to decompose the remaining azimuth space-variant phase, and the first two feature components after SVD are integrated to make them be represented by a new feature component. Finally, the new feature component is used for interpolation to correct the azimuth space-variance. The simulation results show that the proposed CSVD can further improve the image quality compared with SVD-Stolt.

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Real-Time Processing of Spaceborne SAR Data With Nonlinear Trajectory Based on Variable PRF

Cited by 69 publications

References 32 publications

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network

Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm

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