Synthetic Aperture Radar Imaging System Research Articles

Multifrequency Compressed Sensing for 2-D Near-Field Synthetic Aperture Radar Image Reconstruction

This paper investigates a new multifrequency compressed sensing (CS) model for 2-D near-field microwave and millimeter-wave synthetic aperture radar (SAR) imaging system, which usually collects multifrequency sparse data. Spatial data of each frequency are represented as a hierarchical tree structure under a wavelet basis and spatial data of different frequencies are modeled as a joint structure, because they are highly correlated. Based on the developed multifrequency CS model, a new CS approach is proposed by exploiting both the intrafrequency and interfrequency correlations, and enriches the existing CS approaches for 2-D near-field microwave and millimeter-wave SAR image reconstruction from undersampled measurements. Combining a splitting Bregman update with a variation of the parallel Fast Iterative Shrinkage-Thresholding Algorithm-like proximal algorithm, the proposed CS approach minimizes a linear combination of five terms: a least squares data fitting, a $\textrm {multi-}\ell _{1}$ norm, a multitotal variation norm, a joint-sparsity $\ell _{21}$ norm, and a tree-sparsity overlapping $\ell _{21}$ norm. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and convergence speed.

Reception and calibration of bistatic SF ISAR imaging system with wideband receiver

The reception and calibration of bistatic stepped-frequency (SF) inverse synthetic aperture radar (ISAR) imaging system with wideband receiver are studied in this study. First, as residual carrier frequency offset and over-sampling will be introduced in the SF linear frequency modulation signal which is received by wideband receiver, a spectrum truncation, and frequency error compensation method is proposed to solve the problems. Second, a calibration method of bistatic SF ISAR imaging system based on accumulated spherical satellite echoes is proposed, since echo signals are modulated by non-ideal amplitude and phase characteristics of radar system channels, which seriously deteriorates the quality of SF imaging result. Theoretical analysis and simulation results indicate that the proposed method can effectively compensate the non-ideal characteristics of radar system channels.

Fully Focused SAR Altimetry: Theory and Applications

In this paper, we introduce the concept, develop the theory, and demonstrate the advantages of fully focused coherent processing of pulse echoes from a nadir-looking pulse-limited radar altimeter. This process, similar to synthetic aperture radar (SAR) imaging systems, reduces the along-track resolution down to the theoretical limit equal to half the antenna length. We call this the fully focused SAR (FF-SAR) altimetry processing. The technique is directly applicable to SAR altimetry missions such as CryoSat-2, Sentinel-3, or Sentinel-6/Jason-CS. The footprint of an FF-SAR altimeter measurement is a narrow strip on the surface, which is pulse limited across track and SAR focused along track. Despite the asymmetry of the altimeter footprint, the fully focused technique may be useful for applications in which one needs to separate specific targets within highly heterogeneous scenes, such as in the case of sea ice lead detection, hydrology, and coastal altimetry applications. In addition, over rough homogeneous surfaces, such as the ocean or ice sheets, the improved multilooking capability of FF-SAR leads to a significant increase in the effective number of looks with respect to the delay/Doppler processing, resulting in better geophysical parameter estimation. The proposed processing technique was verified by processing full bit rate CryoSat-2 SAR mode data over the Svalbard transponder. Hydrology, sea ice, and open ocean applications are also demonstrated in this paper, showing the improvement of this technique with respect to conventional and delay/Doppler altimetry.

SAR Image segmentation based on convolutional-wavelet neural network and markov random field

Synthetic aperture radar (SAR) imaging system is usually an observation of the earths' surface. It means that rich structures exist in SAR images. Convolutional neural network (CNN) is good at learning features from raw data automatically, especially the structural features. Inspired by these, we propose a novel SAR image segmentation method based on convolutional-wavelet neural networks (CWNN) and Markov Random Field (MRF). In this approach, a wavelet constrained pooling layer is designed to replace the conventional pooling in CNN. The new architecture can suppress the noise and is better at keeping the structures of the learned features, which are crucial to the segmentation tasks. CWNN produces the segmentation map by patch-by-patch scanning. The segmentation result of CWNN will be used with two labeling strategies (i.e., a superpixel approach and a MRF approach) to produce the final segmentation map. The superpixel approach is used to enforce the smooth nature on the local region. On the other hand, the MRF approach is used to preserve the edges and the details of the SAR image. Specifically, two segmentation maps will be produced by applying the superpixel and MRF approaches. The first segmentation map is obtained by combining the segmentation map of CWNN and the superpixel approach, and the second segmentation map is obtained by applying the MRF approach on the original SAR image. Afterwards, these two segmentation maps are fused by using the sketch map of the SAR image to produce the final segmentation map. Experiments on the texture images demonstrate that the CWNN is effective for the segmentation tasks. Moreover, the experiments on the real SAR images show that our approach obtains the regions with labeling consistency and preserves the edges and details at the same time.

PRELIMINARY RESULTS OF ESTIMATING SOIL MOISTURE OVER BARE SOIL USING FULL-POLARIMETRIC ALOS-2 DATA

Abstract. Synthetic Aperture Radar (SAR) imaging system is one of the most effective way for Earth observation. The aim of this study is to present the preliminary results about estimating soil moisture using L-band Synthetic Aperture Radar (SAR) data. Full-polarimetric (HH, HV, VV, VH) ALOS-2 data, acquired on 22.04.2016 with the incidence angle of 30.4o, were used in the study. Simultaneously with the SAR acquisition, in-situ soil moisture samples over bare agricultural lands were collected and evaluated using gravimetric method. Backscattering coefficients for all polarizations were obtained and linear regression analysis was carried out with in situ moisture measurements. The best correlation coefficient was observed with VV polarization. Cross-polarized backscattering coefficients were not so sensitive to soil moisture content. In the study, it was observed that soil moisture maps can be retrieved with the accuracy about 14% (RMSE).

Range-Cell-Focusing Algorithm Combined With Enhanced MUSIC for Close Target Imaging

A range-cell-focusing algorithm is proposed in order to improve the quality of the target image. In a synthetic aperture radar (SAR) imaging system, the range resolution depends on the frequency bandwidth and determines the ability to distinguish between targets that are very close to each other. In cases where the resolution and the SNR from the environment are not adequate, targets cannot be accurately visualized. In order to successively classify targets that are close, we are combining an enhanced-multiple-signal-classification spectrum as a weighting function to reproduce the raw data. The proposed algorithm improves classification and separation for close targets while suppressing artifacts in the final images. The targets of interest are stationary point scatterers. The results are obtained from both simulated and experimental data to demonstrate that the proposed algorithm provides better performance than a conventional SAR imaging algorithm, the range migration algorithm.

Phase adjustment for multistatic passive radar imaging based on image entropy and image contrast

ABSTRACTMultistatic passive radar imaging system (MPRIS) does not behave as well as traditional inverse synthetic aperture radar (ISAR) imaging system does on account of the limited illuminators of opportunity and relatively narrow bandwidth of the signals. Furthermore, in the MPRIS, limited by the unavoidable measurement errors of the positions of the illuminators or receivers, or the manoeuvrability of the moving target, the translation motion of the target cannot be compensated accurately, in which the echo will be contaminated by an error phase, resulting in the occurrence of serious image defocus and the degeneracy of the image quality. In this article, two nonparametric autofocusing techniques based on the principles of minimum entropy and maximum contrast, respectively, are proposed to deal with the undesired problems under the multistate geographical configuration consisting of one illuminator and several receivers distributed around a target. The error phase is corrected by introducing an adjustment phase in the frequency domain and the adjustment phase can be searched by iteratively solving an equation that is derived by optimizing the quality measure of the image. Since no assumption is made during the derivation, the proposed techniques could be used to compensate any form of the phase error. In addition, the influence of the multistatic configuration on the image resolution is analysed and an optimized configuration is obtained. The effectiveness of the proposed algorithms is verified via the theoretical derivations and simulations.

SAR Image Segmentation Based on Hierarchical Visual Semantic and Adaptive Neighborhood Multinomial Latent Model

A synthetic aperture radar (SAR) imaging system usually produces pairs of bright area and dark area when depicting the ground objects, such as a building or tree and its shadow. Many buildings (trees) are aggregated together to form urban areas (forests). It means that the pairs of bright and dark areas often exist in the aggregated scenes. Conventional unsupervised segmentation approaches usually segment the scenes (e.g., urban areas and forests) into different regions simply according to the gray values of the image. However, a more convincing way is to regard them as the consistent regions. In this paper, we aim at addressing this issue and propose a new SAR image segmentation approach via a hierarchical visual semantic and adaptive neighborhood multinomial latent model. In this approach, the hierarchical visual semantic of SAR images is proposed, which divides SAR images into aggregated, structural, and homogeneous regions. Based on the division, different segmentation methods are chosen for these regions with different characteristics. For the aggregated region, locality-constrained linear coding-based hierarchical clustering is used for segmentation. For the structural region, visual semantic rules are designed for line object location, and a geometric structure window-based multinomial latent model is proposed for segmentation. For the homogeneous region, a multinomial latent model with adaptive window selection is proposed for segmentation. Finally, these results are integrated together to obtain the final segmentation. Experiments on both synthetic and real SAR images indicate that the proposed method achieves promising performances in terms of the consistencies of the regions and the preservations of the edges and line objects.

Optimal waveform-based clutter suppression algorithm for recursive synthetic aperture radar imaging systems

A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.

Synthetic aperture radar image of fractal rough surface

The synthetic aperture radar imaging of fractal rough surface is studied. The natural surface can be very accurately described in terms of fractal geometry. Such a two-dimensional fractional Brownian motion (FBM) stochastic process provides a very sound description of natural surface. The samples of band-limited FBM process are realized by using physical Weierstrass-Mandelbrot function. The parameters of fractal rough surface are discussed and how to choose the value is analyzed. The roughness is mostly determined by the Hurst coefficient or the fractal dimension. In the actual simulation, a fractal rough surface can be seen as the superposition of finite sinusoidal tones, and any scattering measurement is limited to a finite set of scales. In this paper, the surface is described with two-scale model, i. e., locally approximated by plane facets with dimension smaller than that of resolution cells, but much larger than wavelength. Because this paper focuses on the texture of the synthetic aperture radar (SAR) image and the overall image texture is related to the macroscopic scale, the microscopic roughness superimposed on the facets is neglected. For the macroscopic scale scattering problem, a facet Kirchhoff approach is proposed. The fractal rough surface consists of many triangle facets, and the scatter field of each facet can be obtained by the facet Kirchhoff approach. The principle of dimension selection is studied. The dimension of facet must follow the principle that the surface profile is not damaged. At the same time, the facet dimension should be as large as possible in order to increase the efficiency of imaging. After establishing the fractal geometry model and obtaining the field from each facet, the SAR image can be realized through Rang-Doppler method in the stripmap mode. The results show that in the SAR image, the effects of fractal parameters on the rough surface can be obviously observed. The peaks and ravines of rough surface are obviously observed at low fractal dimension or high Hurst coefficient. However, when the fractal dimension gets higher or Hurst coefficient gets higher, the peaks and ravines disappear because the surface becomes rougher and diffuse scattering is enhanced. The effect of fractal parameter on the SAR image can be specifically expressed with entropy and angle second moment. With the increase of fractal dimension D, the texture of SAR image behaves more randomly and disorderly. So the entropy of SAR image becomes larger and the angle second moment of SAR image becomes smaller. The texture of SAR image is also related to the squint angle and frequency of incidence wave. The relative roughness will become larger when the squint angle and frequency of incidence wave become larger. The research on a complete fractal surface SAR imaging system consists of establishing the environmental model, developing the electromagnetic scattering model, and using the SAR imaging technique. The achievements show the characteristics of fractal rough surface SAR image, which have a theoretical support for natural environment remote sensing and the environment parameters inversion.

SAR imaging method based on coprime sampling and nested sparse sampling

As the signal bandwidth and the number of channels increase, the synthetic aperture radar (SAR) imaging system produces huge amount of data according to the Shannon-Nyquist theorem, causing a huge burden for data transmission. This paper concerns the coprime sampling and nested sparse sampling, which are proposed recently but have never been applied to real world for target detection, and proposes a novel way which utilizes these new sub-Nyquist sampling structures for SAR sampling in azimuth and reconstructs the data of SAR sampling by compressive sensing (CS). Both the simulated and real data are processed to test the algorithm, and the results indicate the way which combines these new undersampling structures and CS is able to achieve the SAR imaging effectively with much less data than regularly ways required. Finally, the influence of a little sampling jitter to SAR imaging is analyzed by theoretical analysis and experimental analysis, and then it concludes a little sampling jitter have no effect on image quality of SAR.

Simulation of the SuperSAR Multi-Azimuth Synthetic Aperture Radar Imaging System for Precise Measurement of Three-Dimensional Earth Surface Displacement

The SuperSAR imaging system, a novel multi-azimuth synthetic aperture radar (SAR) system capable of detecting Earth surface deformation in three dimensions from a single satellite platform, has recently been proposed. In this paper, we investigate the feasibility of detecting precise 3-D surface displacement measurements with the SuperSAR imaging system using a point target simulation. From this simulation, we establish both a relationship between the interferometric SAR phase and the across-track displacement and a relationship between the multiple-aperture interferometry phase and the along-track displacement based on the SuperSAR imaging geometry. The theoretical uncertainties of the SuperSAR measurement are analyzed in the across- and along-track directions, and the theoretical accuracy of the 3-D displacement measurement from the SuperSAR system is also investigated according to both the decorrelation and the squint and look angles. In the case that the interferometric coherence is about 0.8 and that five effective looks are employed, the theoretical 2-D measurement precision values are about 3.67 and 6.35 mm in the across- and along-track directions, respectively, and the theoretical 3-D measurement precision values for 3-D displacement are about 4.05, 4.56, and 3.45 mm in the east, north, and up directions, respectively. The result of this study demonstrates that the SuperSAR imaging system is capable of measuring the 3-D surface displacement in all directions with subcentimeter precision.

ISAR Imaging Based on the Wideband Hyperbolic Frequency-Modulation Waveform.

The hyperbolic frequency-modulated (HFM) waveform has an inherent Doppler-invariant property. It is more conducive than the conventional linear frequency-modulated (LFM) waveform to high speed moving target imaging. In order to apply the HFM waveform to existing inverse synthetic aperture radar (ISAR) imaging systems, a new pulse compression algorithm is proposed. First, the received HFM echoes are demodulated with the transmitted signal, which is called “decurve” in this paper. By this operation, the bandwidth of the demodulated echoes is effectively reduced and can be processed by the existing narrow-band receiver. Then, the phase of the decurved HFM echoes is analyzed, and thus, the pulse compression is accomplished by space-variant phase compensation. In addition, the space-variant phase compensation is realized by resampling and fast Fourier transform (FFT) with high computational efficiency. Finally, numerical results illustrate the effectiveness of the proposed method.

Measurement and Correction of the Ionospheric TEC in P-Band ISAR Imaging

It is commonly known that the ionosphere has significant effects on a low-frequency (particularly P-band) radar signal. It causes the degradation of the image quality in synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging systems. In this letter, we analyze the ionospheric effects on radar signals and find that the total electron content (TEC) is a key to the ionospheric effects. A method is proposed to evaluate the TEC from a received ISAR signal and to correct the ionospheric effects. Some real experimental results, using a ground-based P-band ISAR system to observe a space target in the ionosphere, are used to validate the proposed method.

A Fast Radial Scanned Near-Field 3-D SAR Imaging System and the Reconstruction Method

This paper presents a near-field 3-D synthetic aperture radar (SAR) imaging system for which the 2-D aperture is radially scanned. Compared to the current SAR imaging systems, the proposed system has several advantages such as quick data collection, full 360° inspection of target, and simple image formation processing. However, in radial scan, the samples do not fall on a Cartesian grid, which prevents us from using the fast Fourier transform (FFT) to form SAR image without calling for interpolation. In this paper, the 2-D nonuniform FFT (NUFFT) is used for dealing with the problem. After 2-D NUFFT of the radial sampled data, the 3-D reflectivity image can be efficiently reconstructed by using the 3-D version of the range migration algorithm. The Stolt mapping has been implemented implicitly by another 1-D NUFFT to reduce the artifacts caused by the conventional interpolation processing. In addition, to alleviate the data sampling burden, a compressed 2-D slow-time sampling strategy is also discussed. Finally, the proposed Rad-SAR system and the imaging method are demonstrated using near-field wideband simulation data.

Synthetic Aperture Radar Imaging Using Basis Selection Compressed Sensing

A compressed sensing method with basis selection is applied to a millimeter-wave synthetic aperture radar (SAR) imaging system. With a large candidate set of bases to choose from and without any a ...

Image registration of interferometric inverse synthetic aperture radar imaging system based on joint respective window sampling and modified motion compensation

We propose a new image registration method based on joint respective window sampling (RWS) and modified motion compensation (MMC) in an interferometric inverse synthetic aperture radar (InISAR) imaging system using two antennas. The causation and quantitative analysis of the offset between two ISAR images of different antennas along the baseline are analyzed. In the proposed method, the RWS method, according to the measured distance between the target and different antennas, compensates the offset in the range direction. The MMC method is adopted to eliminate the offset in the Doppler direction. Simulation results demonstrate that the offset between the two ISAR images can be compensated effectively, consequently achieving a high-quality three-dimensional InISAR image.

MIMO SAR Using Orthogonal Coding: Design, Performance Analysis, and Verifications

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is a promising technology in radar imaging which provides a better balance of azimuth resolution and swath width compared with traditional single-input single-output (SISO) SAR. It has the potential to help scientists and engineers to design ambitious SAR system with higher resolution and wider swath. This paper studies the principle of MIMO SAR using orthogonal coding waveform and then provides the performance analysis in resolution and swath width. By using orthogonal coding waveform, lower channel interference is obtained, which makes MIMO SAR achieve wider unambiguous range swath and lower azimuth ambiguity. Simulations are carried out by means of the system parameters of real spaceborne SAR platform. A ground-based MIMO SAR imaging system with up and down chirp modulation is also designed. The performances of MIMO SAR and SISO SAR are compared, and the validity and advantage of MIMO SAR are verified.

Multichannel HRWS SAR Imaging Based on Range-Variant Channel Calibration and Multi-Doppler-Direction Restriction Ambiguity Suppression

In order to obtain high-resolution wide-swath (HRWS) images, the multichannel in azimuth synthetic aperture radar (SAR) system has been adopted to deal with the contradiction problem between high resolution and low pulse repetition frequency (PRF). In this paper, a novel channel-calibration method is proposed for the multichannel in azimuth HRWS SAR imaging system. During the channel calibration, the mismatch between the channels, which results from the gain-phase error and the range sampling time error, is first corrected by the coarse-calibration processing in the range frequency domain. Then, the along azimuth baseline measurement error is estimated. Considering the range variance in the residual phase error, the data are processed in blocks along the range time domain, and the error of every subblock data is estimated. After that, a fitting and filtering is implemented along the range to the estimated values of the phase error of all subblocks. The range-variant phase error is then compensated using their estimated values. After channel calibration, this paper also presents a new Doppler ambiguity suppression algorithm which nulls the ambiguity components in the Doppler domain. The newly proposed algorithm outperforms the post-Doppler ambiguity suppression algorithm. The airborne real measured scan synthetic aperture radar data, which are acquired by a seven-channel in azimuth SAR imaging system with the system working at X-band, are utilized to demonstrate the performance of the newly proposed channel-calibration method and the new Doppler ambiguity suppression algorithm.

Compressed sensing for SAR‐based wideband three‐dimensional microwave imaging system using non‐uniform fast Fourier transform

A new compressed sensing (CS) image reconstruction method is proposed for high-resolution wideband threedimensional synthetic aperture radar (SAR) imaging systems. In contrast to existing CS SAR methods that employ only a forward SAR transform in pre- or post-processing, the proposed method employs both forward SAR and reverse SAR (R-SAR) transforms in each CS iteration to improve the quality of reconstructed images. This study proposes a simple and elegant truncation repair method to combat the truncation error and utilises non-uniform fast Fourier transform to reduce the SAR and R-SAR transform errors, thereby ensuring the convergence of the CS algorithm and improving the quality of the reconstructed images. The proposed CS SAR method is applied to microwave and millimeter wave imaging systems for non-destructive evaluation of materials embedded in stratified media. Three different specimens under test are measured by conventional uniform sampling and by random under-sampling with 20% or 30% spatial points of the uniform sampling. The reconstructed images show that, albeit having much less measurement points, the proposed CS method achieves better image quality and lower background artefacts than the images reconstructed from the fullysampled uniform measurements.