Radio frequency interference (RFI) is an important factor affecting microwave remote sensing observations, causing random degradation of synthetic aperture radar (SAR) images. Due to the huge amount of raw echo and single-look complex (SLC) data, there are some SAR interpretation scenarios when only amplitude images can be obtained, and traditional signal transformation and matrix operations can hardly meet the suppression requirements in the absence of phase information at this time. Although deep learning algorithms have made some progresses on this issue, they still suffer from the following limitations: (i) There are few models dedicated to SAR RFI in the image domain; (ii) The network structure is relatively complex due to not fully exploit the physical characteristics of SAR and RFI. To this end, we propose an end-to-end suppression network (PMNet), which includes a novel explainable feature decomposition module (FDM) based on the idea of non-negative matrix factorization and a composite loss function to achieve dynamic separation of foreground and background features of supervised RFI-contaminated images. The ablation experiment proves that compared with the baseline algorithm, the visual similarity of the proposed PMNet on the test set can be improved by up to 14.15%. The suppression result on Sentinel-1 and Gaofen-3 real data also verifies the effectiveness of the PMNet in different SAR platforms.

Multiaspect synthetic aperture radar (SAR) observations of complex targets often exhibits distinct anisotropic feature, which also contains rich information about target structural complexity. In practical scenarios, SAR images at certain aspects may be unsampled or may not be available, in such case, interpolation of SAR images from other sampled or available aspects, known as multiaspect interpolation, is needed. This article conducts a preliminary study on the quantitative description of anisotropic scattering variation (ASV) and the relationship between ASV and multiaspect interpolation based on typical aircraft target airborne multiaspect SAR dataset. The aim is to clarify when it is more meaningful to perform multiaspect interpolation and propose ASV operator to describe the variation of anisotropic scattering, which, to some extent, can also express target structural complexity. In the exploration process, it was found that certain expression of anisotropic feature can reflect the structural and textural feature of targets, which can represent changes of anisotropic scattering. Therefore, these expressions are incorporated into ASV operator, which makes the operator effective. Experimental results show that the proposed ASV operator can accurately describe changes of anisotropic scattering based on aircraft target data. Moreover, it can predict the effectiveness of interpolated SAR images based on the value of ASV operator. Consequently, multiaspect interpolation of SAR images at required aspects can be performed based on prior predictions using sampled SAR images.

Spaceborne spotlight SAR mode has advantages of relative wide coverage and high resolution. Moving target imaging using spaceborne SAR system is important in both civil and military applications. Current researches focus on ground and maritime target like vehicle and large ship, the topic about aeroplane is rear. Therefore, based on Chinese GF-3 spotlight SAR SLC data, a new moving aeroplane imaging method is proposed. the moving target signal model in spotlight SAR SLC is deduced, the residual range migration and azimuth quadratic phase are then analyzed. it turns out that the residual range cell migration and quadratic phase relate to the relative speed. then, the moving aeroplane can be focused iteratively via tuning the relative speed parameter. The GF-3 spotlight SAR data of a non-cooperative aeroplane is used to validate proposed method.

An innovative approach, mainly featured by progressive iterations of partitioning and focusing, to processing very-high-resolution spaceborne synthetic aperture radar (SAR) data is presented in this article. Due to long integration time endured during data acquisition as well as large scene extension being common to the advanced SAR systems, spatial dependence of range histories may be severe, especially is the terrain undulation severe, and must be properly dealt with. In this article, after deriving the exact two dimensional spectrum for an curved satellite orbit, we focus the entire echo data through several iterations of partitioning and focusing. Besides, a new technique aiming at enhance the focusing quality of the final data blocks is also presented. Finally, the well-focused final image blocks are recombined into the entire image. Owing to the feature of partitioning, the spatial dependence of many factors can be easily accommodated. The methodology is validated by processing results on simulated and real SAR data.

The existence of Radio Frequency (RF) Interference will cause an adverse effect on the interpretation of Synthetic Aperture Radar (SAR) images. There are various types of interference, and their pattens in images vary in different situations. Previous algorithms have disadvantages of low precision and large amount of computation. In this paper, we propose a prior-induced deep neural network. Based on the sparse and low-rank properties of interference signals in the time-frequency domain, an interference suppression network is designed to reconstruct useful signals. At the same time, a new loss function is designed, which integrates the sparse and low-rank properties with the training of network. The network combines the idea of semi-parametric interference suppression and the deep learning method, which can make good use of the characteristics of SAR echoes, making it more suitable for the field of signal processing and having a better effect. The proposed algorithm is applied to real SAR data with interference to validate its effect and efficiency.

Radio frequency electromagnetic interference is a relatively common phenomenon, especially for synthetic aperture radar (SAR) systems working in P- or L-band. Compared with the suppression of narrowband interference, that of wideband interference, particularly of those whose signal parameters have frequently changing property, is still a sophisticated problem. In this letter, a suppression algorithm for interference with wideband and complicated parameters is proposed, based on low-rank and sparse matrix decomposition (LRSMD) in time–frequency domain (TFD) of the signal. The proposed algorithm begins with transforming the SAR signal into TFD. After that, LRSMD based on bilateral random projection (BRP) is applied to decompose the time–frequency spectrum matrix into three parts, a low-rank matrix standing for interference, a sparse matrix standing for SAR signal, and a noise matrix. Finally, inversely transform the sparse matrix into the time domain to obtain SAR signal without interference. The proposed algorithm is applied to a single look complex (SLC) SAR data of Sentinel-1 to validate its effect and efficiency.

Remote sensing image-based ship detection is an important tool for ocean surveillance. Geostationary orbit (GEO) satellite is characteristic with a high temporal resolution, which makes continuous monitoring possible. However, the ships in such images are generally quite tiny. Traditional methods usually detect tiny ships with the character of shape, lightness and contrast, which are not resistant to false alarms from fractus. In this letter, a network-based detection method is adopted to extract spatio-temporal jointly features to distinguish the real ships from other distractions, especially in fractus concentered scenes. Based on detection results, an intersection over union (IoU) based target matching method is proposed to form the trails, suppressing the false alarms at the same time. To reduce the false alarm further, a structure similarity (SSIM) based appearance consistency measurement is utilized to remove objects whose appearance change over time. The experiment results show that the proposed method detects and tracks ships with high recall and low false alarm, compared with the traditional tracking methods. It could be applicated in GEO remote sensing images based ocean surveillance in various kinds of scenes.

In the complex electromagnetic environment, radio frequency interference (RFI) from other radiation sources often conflicts with synthetic aperture radar (SAR) systems, which overlaps and destroys the useful data in the same frequency band, causing adverse impact to the quality of SAR imaging. When faced with wideband or mixed complicated RFI, traditional methods inevitablely damage the original signal, and cannot effectively protect and reconstruct the useful information. Besides, the current semi-parametric algorithms have large computations and limited generalization ability. To address these issues, this paper proposes a prior-induced-learning framework (PISNet) to achieve RFI suppression and useful signal recovery in time-frequency domain. Both narrowband and wideband interference are uniformly modeled as a sparse distribution in time-frequency domain, and the stationarity of SAR echoes determines its low-rank characteristic. These properties of RFI and SAR data are treated as prior knowledge to inject into our PISNet. An iterative reconstruction module is raised to achieve low-rank reorganization of the fused residual features. Meanwhile, a novel loss function is put forward to induce the network training to ensure that each component conform to the prior. The proposed approach innovatively integrates deep learning with semi-parametric methods for RFI suppression, which achieves superior performance on simulated and real data. Compared to existing learning-based methods, the image quality of the restored Sentinel-1 data is improved by 9.37% AG. The code and dataset will be available online (https://github.com/JyuanShen/PISNet).

Spotlight synthetic aperture radar (SAR) achieves very high-resolution (VHR) images by steering the azimuth beam during the formation of the synthetic aperture. In practice, the steering is implemented through discrete azimuth beam switching. Then, phase shifts can occur between the adjacent beams due to the error of the antenna pattern. In addition, the troposphere introduces a beam-angle-dependent delay to the echo. Those undesired phase shifts and delays cause phase errors in the received echo and result in image quality deterioration. In this letter, an algorithm, called the Newton entropy minimization (N-EM), is proposed to estimate and compensate the phase errors caused by the discrete azimuth beam steering for the spotlight SAR data. Combining with the subaperture imaging approach of the spotlight SAR, the algorithm estimates the phase offsets between each couple of the adjacent beams based on the minimum entropy criterion. The analytic expression, which is a nonlinear equation, is developed for the optimal estimation. Then, the Newton's method is employed to solve the equation. The real spaceborne SAR (both the staring and sliding spotlight modes) data processing results demonstrate the efficiency and accuracy of the proposed algorithm.

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method.

Circular Synthetic Aperture Radar(CSAR) has become a hotspot with its characteristic of elevation plane resolution and all-aspect observing ability. Digital elevation model (DEM) extraction in urban arears by using single-pass CSAR data without requiring additional knowledge is a subject of interest. The target, whose real height is not equal to the reference imaging height will project to different locations after imaging in different sub-aperture. In this paper, the quantitative relationship between offset of imaging points and height difference is deduced theoretically in the real scene, where the airborne SAR platform trajectory is not a standard circle. DEM of an area is presented using the data acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS). Compared with the DEM provided by the German Aerospace Center (DLR) with 1m absolute height error, the effectiveness of the proposed method is verified.

Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, L1regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets' intensities cannot kept. The underestimation will also cause radiometric errors and affect the quantitative use of the SAR data. In this letter, we present a SAR imaging method based on generalized minimax concave (GMC) penalty. GMC is a nonconvex penalty and its cost function is convex. GMC can avoid the underestimation of pixel intensity. In the iteration, the azimuth-range decouple operators are used to avoid the huge memory and computational costs. The performance of the proposed method is verified using real data.

A new image formation algorithm is proposed for processing very high resolution spaceborne sliding-spotlight synthetic aperture radar (SAR) data. Because of along-track antenna steering, the Doppler bandwidth of the received SAR data is expanded significantly beyond one pulse repetition frequency interval. Furthermore, the range histories become spatially dependent in both dimensions and cannot be expressed exactly by a hyperbolic model. In our approach, we first reduce the Doppler bandwidth by a novel azimuth dechirp processing method in the range frequency domain. The data are then processed by the standard ω-κ algorithm with a fixed effective velocity. Thereafter, the chirp modulation concept is imported to rebuild new data with much shorter apertures. Finally, a standard back-projection algorithm is employed to accumulate the signal pixel by pixel along the newly built aperture. Thus, the balance between processing efficiency and precision can be controlled by adjusting the length of the new apertures. In addition, a more accurate 2-D spectrum derivation is employed to enhance the processing precision, and a novel range-splitting method is presented to accommodate the range dependence of effective velocities. Furthermore, when implementing the back projection, the image grid-the region and granularity level of which are user defined-is placed on the earth's surface instead of on the slant-range plane, and the routine geometry projection processing thus becomes dispensable.

GF-3 is the Chinese Synthetic Aperture Radar (SAR) satellite mission with scientific and commercial applications, which was launched in August, 2016. In this paper, various error sources about system position are analyzed based on real data of GF-3 satellite. The results show that satellite positioning accuracy is less than 4m.

Existing low-Earth-orbit synthetic aperture radar (SAR) algorithms generally assume that the data are azimuth invariant. However, this assumption does not hold for the medium-Earth-orbit (MEO) SAR systems due to the significantly longer azimuth integration time and complex imaging geometries. As a result, the MEO SAR data cannot be processed accurately and efficiently using the existing algorithms. To solve this problem, this letter proposes a two-step azimuth perturbation (AP) method that uses the first-step AP to remove the bulk azimuth variance at the range processing stage and the second-step AP to remove the residual variance at the azimuth processing stage. As an example, an improved range Doppler algorithm with the integrated two-step AP is discussed in this letter. Simulations of an L-band MEO SAR with 5-m resolution at 10 000-km orbit height are used to demonstrate the validity and accuracy of this algorithm.

Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on Keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.

The layover phenomenon of SAR imaging arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the scenarios. Because of the phase discontinuities, for single baseline interferometric SAR, it is difficult to recover the accurate unwrapped phase in layover areas by traditional phase unwrapping methods. In this paper, according to the phase characteristic of layover areas, we propose a new method to retrieve unwrapped phase based on local frequency estimate. It can avoid the unwrapping error resulted from the phase jump at the edge of layover areas. As a result, relative accurate DEM of layover areas can be reconstructed, which is beneficial to afterward geocoding in InSAR topographic mapping.