Sidelobe Suppression with Resolution Maintenance for SAR Images via Sparse Representation

Zhu, Xiaoxiang; He, Feng; Fan, Yi; Dong, Zhen; Wu, Meiping

doi:10.3390/s18051589

Cited by 14 publications

(12 citation statements)

References 22 publications

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“…Since the GF-3 satellite launched on 10 August 2016 by the China Academy of Space Technology (CAST), many researches have been performed on these multi-mode SAR images from the GF-3 satellite. Zhu et al [ 3 ] proposed a method of sidelobe suppression based on the sparsity constraint regularization to reduce sidelobes of GF-3 images in sea areas. In addition, three different restriction principles were proposed as a joint one in [ 4 ] to keep the image sharpness while preserving the scattering mechanism as well as speckle reduction of GF-3 images.…”

Section: Introductionmentioning

confidence: 99%

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis

Jiao

Wang

You

et al. 2018

Sensors

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The GaoFen-3 (GF-3) satellite is the only synthetic aperture radar (SAR) satellite in the High-Resolution Earth Observation System Project, which is the first C-band full-polarization SAR satellite in China. In this paper, we proposed some error sources-based weight strategies to improve the geometric performance of multi-mode GF-3 satellite SAR images without using ground control points (GCPs). To get enough tie points, a robust SAR image registration method and the SAR-features from accelerated segment test (SAR-FAST) method is used to achieve the image registration and tie point extraction. Then, the original position of these tie points in object-space is calculated with the help of the space intersection method. With the dataset clustered by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we undertake the block adjustment with a bias-compensated rational function model (RFM) aided to improve the geometric performance of these multi-mode GF-3 satellite SAR images. Different weight strategies are proposed to develop the normal equation matrix according to the error sources analysis of GF-3 satellite SAR images, and the preconditioned conjugate gradient (PCG) method is utilized to solve the normal equation. The experimental results indicate that our proposed method can improve the geometric positioning accuracy of GF-3 satellite SAR images within 2 pixels.

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Section: Introductionmentioning

confidence: 99%

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis

Jiao

Wang

You

et al. 2018

Sensors

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“…This time, the Gaussian noise is added to the simulated signal and makes the signal-to-noise ratio 15 dB. The original radar image, the enhancing results of the CF algorithm [23,24,32,33], the results of the orthogonal matching pursuit (OMP) sparsity driven method [27], and the Lucy-Richardson deconvolution algorithm [28] are shown in Figure 14a-e, respectively, and the results of the CNN and the SV-CNN are shown in Figure 14f,g, respectively. Besides, the MSLLs of these methods are listed in Table 2.…”

Section: Comparison To Other Existing Methodsmentioning

confidence: 99%

Convolutional Neural Network with Spatial-Variant Convolution Kernel

et al. 2020

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Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local position of the motifs. This makes the convolutional neural networks widely used in image processing fields such as image recognition, handwriting recognition, image super-resolution, and semantic segmentation. They also perform well in radar image enhancement. However, the local position invariant character might not be good for radar image enhancement, when features of motifs (also known as the point spread function in the radar imaging field) vary with the positions. In this paper, we proposed an SV-CNN with spatial-variant convolution kernels (SV-CK). Its function is illustrated through a special application of enhancing the radar images. After being trained using radar images with position-codings as the samples, the SV-CNN can enhance the radar images. Because the SV-CNN reads information of the local position contained in the position-coding, it performs better than the conventional CNN. The advance of the proposed SV-CNN is tested using both simulated and real radar images.

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“…To further illustrate the performance of the proposed method, azimuth resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) [17] were given as follows. As shown in Table 2, the azimuth resolution using the proposed method was 7.04% slightly higher than that for the rectangular window, at about 1.85 m. PSLR of the images weighted by rectangular window and Taylor window were −13.28 dB and −25.41 dB.…”

Section: Performance Simulationmentioning

confidence: 99%

Azimuth Sidelobes Suppression Using Multi-Azimuth Angle Synthetic Aperture Radar Images

Wang

Yang

Chen

et al. 2019

Sensors

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A novel method is proposed for azimuth sidelobes suppression using multi-pass squinted (MPS) synthetic aperture radar (SAR) data. For MPS SAR, the radar observes the scene with different squint angles and heights on each pass. The MPS SAR mode acquisition geometry is given first. Then, 2D signals are focused and the images are registered to the master image. Based on the new signal model, elevation processing and incoherent addition are introduced in detail, which are the main parts for azimuth sidelobes suppression. Moreover, parameter design criteria in incoherent addition are derived for the best performance. With the proposed parameter optimization step, the new method has a prominent azimuth sidelobes suppression effect with a slightly better azimuth resolution, as verified by experimental results on both simulated point targets and TerraSAR-X data.

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Sidelobe Suppression with Resolution Maintenance for SAR Images via Sparse Representation

Cited by 14 publications

References 22 publications

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis

Convolutional Neural Network with Spatial-Variant Convolution Kernel

Azimuth Sidelobes Suppression Using Multi-Azimuth Angle Synthetic Aperture Radar Images

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