Speckle is a significant problem in synthetic aperture radar (SAR) image processing, as it deteriorates the texture details and thus limits the precision of its high-accuracy applications. The impact of speckle on SAR images is related to their structural characteristics, which usually classify the images into homogeneous regions, heterogeneous regions, and extremely heterogeneous regions. In the latter two regions, structures such as edges and strong point targets generate a more complex speckle distribution which differs from that in homogeneous regions. Despeckling filters based on multiplicative models perform well in homogeneous regions but exhibit poor performance in the other two types of regions. To solve this problem, a collaborative optimization for SAR image despeckling with structure preservation is proposed in this article, which leverages the coupling between the despeckling module and the structure extraction module. Guided by structure information, the proposed method achieves better preservation of edges and texture details while removing speckle. Experimental results based on real SAR images demonstrate that the proposed method effectively enhances the despeckling performance in all three types of regions.

The azimuth multichannel synthetic aperture radar (SAR) technique can simultaneously achieve high resolution and wide swath, which is important for disaster management, sea and land traffic observation, and environmental monitoring. However, nonuniform sampling leads to multichannel imaging quality degradation such as serious ghost targets or deterioration of signal-to-noise ratio (SNR). In this article, a multichannel SAR imaging algorithm is proposed. The key is to perform spectral selection and extrapolation based on the time-frequency characteristics of multichannel signals under nonuniform sampling conditions to obtain subimages with different ghost distributions. By implementing subimage fusion, the final imaging result can be obtained. Simulations and satellite real data experiments are conducted to verify the effectiveness of the proposed algorithm. The imaging performances of the proposed algorithm in terms of ghost-to-real target ratio (GRTR), and SNR are investigated with respect to the pulse repetition frequency (PRF). Moreover, simulation results also demonstrate that the proposed algorithm exhibits a more robust and consistent overall performance than existing methods.

The minimum-energy-criterion (MEC) algorithm introduces the correlation between frequency points to the reconstruction process and uses the optimization method to realize a good reconstruction of multichannel signals in the nonuniform sampling scenario. However, the reconstruction performance of the MEC algorithm will change with pulse repetition frequency (PRF). In this paper, we proposed an improved minimum-energy-criterion (IMEC) algorithm that sets the appropriate reconstruction bandwidth according to the system requirements, which achieves a more robust reconstruction performance than the MEC algorithm.

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only affects the acquisition of SAR image information, but also increases the difficulty of image interpretation and analysis, and greatly reduces the efficiency of image segmentation and feature classification. Based on the nonlocal idea of SAR-BM3D and the concept of sparse representation, a new synthetic aperture radar (SAR) image speckle noise suppression algorithm is proposed. The algorithm follows the framework structure of SAR-BM3D, performs logarithmic conversion after the coarse filtering in the first step, replaces the three-dimensional similar block group after the similar block matching in the second filtering step with a two-dimensional matrix stacked by columns, and replaces the local linear minimum mean square error (LLMMSE) denoising with K-SVD sparse representation image denoising for speckle noise suppression of SAR images. The experimental results show that the non-local denoising method based on sparse representation achieves better results in terms of evaluation metrics and visual perception quality compared with several existing advanced reference techniques.

Bistatic geosynchronous synthetic aperture radar (GEO SAR) system with a stationary receiver is capable of continuous observation and frequent revisit over certain area, which can be applied in disaster monitoring. The mismatch between the true height and the assumed height of the imaging plane may result in azimuth defocusing. This paper exploit the azimuth defocusing effect to estimate the height of strong scatterers. By estimating and compensating the quadratic phase error, fine imaging results in the reference plane are obtained. The height can then be retrieved accurately combining the phase error and the imaging position in the imaging plane. Simulation based on point targets verifies the proposed method.

Geosynchronous synthetic aperture radar (GEO SAR) system has the ability of observing targets for several hours in a large field of view. Complex and irregular rotating motion of the ship on sea in long aperture time makes it difficult to focus. To accomplish the imaging of maneuvering ships, a novel imaging algorithm is proposed in this article. A spatial filter constructed by convolutional neural network (CNN) is utilized to extract the time–frequency features. Combined with local generalized Radon–Fourier transform (GRFT) and phase gradient autofocus (PGA), the extraction accuracy further increases. Validation results based on simulation experiment, airborne experiment, and wave pool experiment show that the proposed algorithm achieves good imaging performance.

SAR based on compressed sensing (CS) greatly reduces the amount of data. The pseudo-random space-time modulation technology could alleviate the constraint on the type of the observed scene. This paper provides a survey on the effects of noise, sparsity, and phase on the modulation technology performance. Selection of a suitable algorithm is necessary to achieve this goal. l1-norm algorithm performs the best of the three algorithms, including greedy algorithm, and Bayesian algorithm. The experiment results show that the performance of the pseudo-random space-time modulation SAR is improved. With the increase of the noise and the decrease of the sparsity, the performance improvement with modulation is more and more limited. With the decrease of phase density, the performance obtained by modulation decreases continuously. When the amplitude variation range exceeds [0,π], the improvements are similar.

Range and azimuth ambiguities are contradictory in SAR system, which restrict the improvements of resolution and swath in satellite platform. Multichannel needs big and complex antenna. And it is proved that orthogonal transmitting signals cannot suppress range ambiguity. Thus high resolution and wide swath system is still a challenge in SAR development. In this paper, we design a novel range ambiguity suppressing method with single channel, which transmits amplitude modulation chirp signals. Through frequency filtering, the ambiguity images can be separated. A minimum mean square error algorithm is then proposed to restore the resolution robustly. Experiment based on Radatsat-1 echo shows that the proposed method can reduce the range ambiguity by 20 dB. Using this method, single-channel system can extend the swath width twice.

Long integration time of GEOSAR makes it difficult to deal with the maneuvering target due to the complex relative motion between radar and target. This paper addresses the motion parameters estimation problem of high speed maneuvering target in GEOSAR where translation motion and rotating motion are considered. A method based on Keystone transform, Fractional Fourier Transform (FrFT) are introduced to estimate and compensate the translational motion and extract micro motion feature. The effectiveness of the method is validated by simulation results.

Due to the effects of the earth's rotation and the satellite's elliptical orbit, the Doppler centroid varies along the orbit in geosynchronous earth orbit synthetic aperture radar (GEO SAR). With an ultrahigh orbit height, the beam may illuminate outside the earth surface with a rotation angle large than 9 degrees. Therefore, the usual attitude steering methods to minimize the Doppler centroid in low earth orbit SAR (LEO SAR) may not be suitable for GEO SAR. In this paper, a novel steering strategy to maximize Nesigma0 for GEO SAR is proposed when the beam illuminates outside the earth surface. The new geometry and the gradient method are applied to demonstrate the method. The ability to stare imaging in geosynchronous orbit SAR is analyzed and verified by using the back projection (BP) algorithm to simulate the imaging principle. Nesigma0 under the new method improved 1dB comparing with the original image.

In order to solve the parameters estimation problem caused by the moving target with the micro motion in the GEOSAR model, a technique based on time-frequency distribution and least squares estimation is introduced. The model of micro-motion part can be seen as a narrow band stochastic process, which is close to the reality of the motion. The method is used to estimate the parameters of the translation which is coupled with the micro motion. Simulation results indicate that the method can estimate the Doppler parameters of translation part efficiently.