Multichannel Synthetic Aperture Radar Imaging Research Articles

Multi-Channel Synthetic Aperture Radar Imaging of Ground Moving Targets Using Compressive Sensing

Integrated with the array technique, multi-channel processing can be applied to synthetic aperture radar of ground moving target imaging (SAR GMTIm), which is very powerful in remote sensing of smart city. To reduce the data sampling amounts, compressive sensing can be used by exploiting a sparse prior of moving targets. In this paper, the SAR GMTIm from data of compressive sampling is addressed by proposing a novel reweighted sparse algorithm. Here, we mainly focus on sparse imaging and clutter suppression for heterogeneous scene of urban areas. In the scheme, the phase of interferogram and the magnitude after displaced phase center antenna are incorporated to derive the weights on sparsity-constraint. Due to the joint usage of magnitude and phase, the proposed reweighted sparse algorithm can improve the performance of clutter suppression. Finally, experiments using the simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing.

The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently. However, this not only neglects the complementary information between signals of each channel or sub-aperture, but also may lead to failure in guaranteeing the consistency of the position of a scatterer in different channel or sub-aperture images which will make the extraction of some scattering information become difficult. By exploiting the joint sparsity of the signal ensemble, this paper proposes a novel CS-based method for joint sparse recovery of all channel or sub-aperture images. Solving the joint sparse recovery problem with a modified orthogonal matching pursuit algorithm, the recovery precision of scatterers is effectively improved and the scattering information is also preserved during the image formation process. Finally, the simulation and real data is used for verifying the effectiveness of the proposed method. Compared with single channel or sub-aperture independent CS processing, the proposed method can not only obtain better imaging performance with fewer measurements, but also preserve more valuable scattering information for target recognition.

A Weighted Backprojection Algorithm for Azimuth Multichannel SAR Imaging

A backprojection algorithm (BPA), a time domain synthetic aperture radar (SAR) imaging algorithm, can be widely used without modification in various imaging modes, like stripmap, spotlight, sliding spotlight, etc. However, BPA also suffers when used in azimuth multichannel SAR due to the nonuniform sampling problem caused by a nonideal pulse repetition frequency. In this letter, we propose a weighted BPA (WBPA) for azimuth multichannel SAR imaging. Derived from the filter-bank-based reconstruction method, WBPA adds a weighting procedure to BPA. Simulations and an airborne SAR data experiment demonstrate that WBPA can perfectly suppress the azimuth ambiguities under band-limited circumstances. WBPA extends the applicable scope of BPA.

Signal reconstruction from recurrent samples in fractional Fourier domain and its application in multichannel SAR

Sampling plays a critical role in remote sensing and signal analysis. In conventional sampling theory, the signal is sampled at a uniform rate at a minimum of twice the signal bandwidth. However, in many multichannel systems such as analog-to-digital converters, synthetic aperture radar (SAR), and synthetic aperture sonar (SAS), it requires that multidimensional signals or digital images be reconstructed from their recurrent samples, and the signals may not be bandlimited in the traditional Fourier domain. In this paper, a reconstruction algorithm for two-dimensional (2D) recurrently sampled signals is proposed in the fractional Fourier domain. This algorithm can handle the situations where the signal is nonbandlimited, and it is extended to the case of undersampling, where the traditional reconstruction algorithms might fail. The algorithm is based on the nonuniform fractional spectrum, and a method to speed up the computation of the nonuniform fractional spectrum is introduced. Reconstruction from recurrent samples in the case of undersampling is illustrated using numerical examples, and an application to multichannel SAR imaging is included to illustrate these results.

Modeling Fast Boat Motion Impact on Satellite SAR MTI Systems

This letter analyzes the problems of imaging small and fast boats with synthetic aperture radar (SAR), which may experience severe defocusing and/or smearing. This situation is encountered, inter alia , in ocean traffic monitoring and surveillance applications where the detection of small and fast vessels is of great interest. The target modeling and impact of moving vessels in SAR images are presented and discussed using simulated results. A matched filter bank is used before applying moving target indication techniques over multichannel SAR images, refocusing the target signal in the azimuth domain and enhancing the detectability of these types of vessels.

Improved Azimuth Multichannel SAR Imaging for Configurations With Redundant Measurements

The traditional spectrum reconstruction algorithm for nonuniform sampling of a displaced phase center antenna (DPCA) synthetic aperture radar (SAR) system can provide an unambiguous recovery of an original signal through the inverse computation of a joint filter matrix. However, when the sampling positions of different receivers coincide spatially, the joint filter matrix will be non-invertible, thus causing the ineffectiveness of the algorithm. This letter proposes a regularized spectrum reconstruction scheme when the pulse repetition frequency of a multichannel system operates on these critical frequencies, as well as in their neighborhoods. First, a low-order regularization of the joint filter matrix is suggested so that the original spectrum of the multichannel system can be recovered more accurately with a decreased number of nonredundant channels' measurements. Furthermore, in order to improve the output signal-to-noise ratio (SNR) of the reconstruction network, a combinational low-order spectrum recovery scheme is additionally presented, which takes all redundant channels' samples into account. The optimizations bring significant benefits to both the suppression of aliased ambiguities and the advance of the output SNR of the reconstruction network.

Unsupervised change detection based on improved Markov random field technique using multichannel synthetic aperture radar images

Change detection represents an important remote sensing tool in environmental monitoring and disaster management. In this respect, multichannel synthetic aperture radar (SAR) data offer great potential because of their insensitivity to atmospheric and sun-illumination conditions (over optical multispectral data) and the improved discrimination capability they may provide compared to single-channel SAR. The problem of detecting the changes caused by flooding is addressed by a contextual unsupervised technique based on a Markovian data fusion approach. However, the isotropic formulation of Markov random field (MRF) models causes oversmoothing of spatial boundaries in the final change maps. In order to reduce this drawback, an edge-preserving MRF model is proposed and formulated by using energy functions that combine the edge information extracted from the produced edge maps using competitive fuzzy rules and Canny technique, the information conveyed by each SAR channel, and the spatial contextual information. The proposed technique is experimentally validated with semisimulated data and real ASAR-ENVISAT images. Change detection results obtained by the improved MRF model exhibited a higher accuracy than its predecessors for both semisimulated (average 12%) and real (average 6%) data.

Exploiting Patch Similarity for SAR Image Processing: The nonlocal paradigm

Most current synthetic aperture radar (SAR) systems offer high-resolution images featuring polarimetric, interferometric, multifrequency, multiangle, or multidate information. SAR images, however, suffer from strong fluctuations due to the speckle phenomenon inherent to coherent imagery. Hence, all derived parameters display strong signal-dependent variance, preventing the full exploitation of such a wealth of information. Even with the abundance of despeckling techniques proposed over the last three decades, there is still a pressing need for new methods that can handle this variety of SAR products and efficiently eliminate speckle without sacrificing the spatial resolution. Recently, patch-based filtering has emerged as a highly successful concept in image processing. By exploiting the redundancy between similar patches, it succeeds in suppressing most of the noise with good preservation of texture and thin structures. Extensions of patch-based methods to speckle reduction and joint exploitation of multichannel SAR images (interferometric, polarimetric, or PolInSAR data) have led to the best denoising performance in radar imaging to date. We give a comprehensive survey of patch-based nonlocal filtering of SAR images, focusing on the two main ingredients of the methods: measuring patch similarity and estimating the parameters of interest from a collection of similar patches.

Adaptive vectorial total variation models for multi‐channel synthetic aperture radar images despeckling with fast algorithms

This study proposes two adaptive vectorial total variation models for multi-channel synthetic aperture radar (SAR) images despeckling with the help of prior knowledge of the image amplitude. Besides despeckling the multi-channel SAR images efficiently, the proposed new models have advantages over other total variation methods in many aspects, such as preserving the radar reflectivity, the targets and edges contrast. The Bermudez-Moreno algorithm and the accelerated fast iterative shrinkage thresholding algorithm are employed to implement the new two models, respectively. Experimental results on multi-polarimetric, multi-temporal RADARSAT-2 images show that the visual quality and evaluation indexes of the proposed models and the corresponding algorithms outperform the other methods with edge preservation.

Unsupervised Coastal Line Extraction From SAR Images

Historically, the extraction of coastal line has been performed exploiting optical images, but in the last two decades, some approaches working with synthetic aperture radar (SAR) data have been proposed. Recently, these approaches have been gaining interest due to the availability of high-resolution SAR images. In this letter, a technique for coastal line retrieval from multichannel SAR images is presented. The detection problem is faced in the statistical estimation framework, in particular, exploiting Bayesian estimation theory. The proposed technique is able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way. The performance of the method has been tested using high-resolution COSMO-SkyMed data sets acquired on the Bay of Naples, showing the high accuracy of the proposed technique.

Demonstration of SAR Distortion Correction Using a Ground-Based Multichannel SAR Test Bed

In this paper, a ground-based phased-array radar is used to investigate the ability of a multichannel synthetic aperture radar (MSAR) to produce high-fidelity images of dynamic ocean scenes by correcting the distortions caused by the motion of the water surface itself. The ground-based system, the Naval Research Laboratory Focused Phased Array Imaging Radar (FOPAIR), mimics an MSAR with multiple apertures arrayed in the flight direction by rapidly and repeatedly scanning across a linear array of 64 antenna elements. This generates a virtually unlimited time stack of coherent images in the same way that an airborne MSAR generates multiple images as the antennas fly past the scene. By manipulating a single FOPAIR data set, both undistorted “benchmark” imagery and distorted images corresponding to an airborne MSAR can be generated. More significantly, the time stack of emulated MSAR images can be processed further using the velocity synthetic aperture radar (VSAR) technique to significantly reduce the well-known but seemingly unavoidable distortions caused by surface wave motion. In this paper, VSAR is demonstrated experimentally for the first time using FOPAIR imagery of a small boat. MSAR systems with a wide range of aperture numbers are emulated, including the special case of a two-aperture system, commonly known as an along-track interferometric SAR. The results emphasize that VSAR processing does not require a long surface coherence time to produce fine-resolution imagery, unlike a single-channel SAR. The results also illustrate some limitations of VSAR as a means to measure velocity and produce high-fidelity imagery of dynamic ocean scenes.

Multichannel synthetic aperture radar signatures and imaging of a moving target

An airborne multichannel synthetic aperture radar imaging process is analyzed to derive spatial image and spectral signatures of a point target moving at a constant velocity. These signatures are expressed in terms of the moving target focal position that changes with the antenna array element location and the relative target-to-radar velocity vector and, as a result, the geometric transformation between two coordinate frames. One frame is stationary while the other moves with the target. The resulting target image spectrum is used to formulate a moving target image refocusing algorithm and a calibration approach useful to compensate the image modulation due to the directional antenna pattern effects. The potential applications of the multichannel signatures to target motion estimation and adaptive image processing will be explored.

The Performance Analysis Based on SAR Sample Covariance Matrix

Multi-channel systems appear in several fields of application in science. In the Synthetic Aperture Radar (SAR) context, multi-channel systems may refer to different domains, as multi-polarization, multi-interferometric or multi-temporal data, or even a combination of them. Due to the inherent speckle phenomenon present in SAR images, the statistical description of the data is almost mandatory for its utilization. The complex images acquired over natural media present in general zero-mean circular Gaussian characteristics. In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. In this context, the eigendecomposition of the multi-channel covariance matrix has been shown in different areas of high relevance regarding the physical properties of the imaged scene. Specifically, the maximum eigenvalue of the covariance matrix has been frequently used in different applications as target or change detection, estimation of the dominant scattering mechanism in polarimetric data, moving target indication, etc. In this paper, the statistical behavior of the maximum eigenvalue derived from the eigendecomposition of the sample multi-channel covariance matrix in terms of multi-channel SAR images is simplified for SAR community. Validation is performed against simulated data and examples of estimation and detection problems using the analytical expressions are as well given.

Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing (CS) shows that it is possible to recover a sparse signal from very limited measurements. Synthetic aperture radar (SAR) imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem. For multichannel SAR imaging based on CS, each channel requires sufficient samples for separate imaging and the total number of samples could still be large. We propose an imaging algorithm based on distributed compressive sensing (DCS) that reconstructs scenes jointly under multiple channels. Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene, but also exploits the correlation among channels. It requires significantly fewer samples than multi-channel SAR imaging based on CS. If multiple channels offer different sampling rates, DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.

Indication of slowly moving ground targets in non-Gaussian clutter using multi-channel synthetic aperture radar

The problem of how best to maximise the ratio of mean target intensity to mean background intensity for slowly moving targets in sets of multi-channel synthetic aperture radar images is discussed for highly non-Gaussian background clutter. The problem is formulated as a direct maximisation of target-to-clutter ratio thus giving a true maximisation of that ratio. Complex-valued weights derived using generalised eigensystem theory are used to maximise the ratio of quadratic forms representing the mean intensity of the target and background derived from their coherence matrices. For two to four channels it is shown that when the target is highly coherent an optimum steering vector is a discrete Fourier transform. For more than two channels it is shown that the optimal solution is only valid within a subspace of the whole parameter space defined by the correlation parameters of the background clutter. Images from a publically released ground moving target indicator dataset are filtered using the results for three channels. The method outperform a standard space-time adaptive processing algorithm in suppressing the stationary background urban clutter image intensity relative to the image intensity because of a known slowly moving ground vehicle. Moreover, the steering vector is much simpler to implement.

Airborne Demonstration of Multichannel SAR Imaging

Multichannel synthetic aperture radar (SAR) makes it possible to obtain high-resolution wide-swath imagery, thus overcoming an inherent limitation of conventional SAR. To cope with a nonuniformly sampled data array in azimuth caused by variations of the pulse repetition frequency, these systems require appropriate coherent processing such as the multichannel reconstruction algorithm. This letter presents the applicability of this algorithm to airborne measured multichannel X-band data. In this context, impact and performance of different channel-balancing methods are investigated. Furthermore, the analytic prediction of residual azimuth ambiguities is verified in the measured data by means of a point target analysis.

On the Iterative Censoring for Target Detection in SAR Images

In this letter, a censoring scheme that iteratively updates the outlier/target maps for target detection in synthetic aperture radar (SAR) images is proposed. For each iteration, any pixels that are indicated by the outlier map as outliers are rejected (censored out) from the clutter estimation. The resulting detected target map is then used as the new outlier map for the next iteration. This procedure is continued until there is no change to the target map, which is then output as the final detection result. The proposed scheme is generically applicable for target detection in both single-channel and multichannel SAR images. In our experiment, in particular, we tested the proposed method on both single-channel and polarimetric SAR data, and its effectiveness was successfully demonstrated.

Suppressing Moving Target Artifacts in Multi-Channel Stripmap SAR Images by Space–Doppler Filtering

Synthetic aperture radar (SAR) as a method for ground imaging by using a single antenna or a sensor array has widely found attraction for remote sensing applications and reconnaissance tasks. Independent of the particular sensor but inherent to the SAR processing, moving objects in the observed scene are imaged at incorrect positions and can appear in a smeared fashion. For countering these disturbing artifacts a joint spatial-spectral processing is proposed and analyzed in this paper that allows to suppress echoes from moving targets in multi-channel stripmap SAR data while preserving the echoes of the fixed scene. Results obtained with experimental data from an airborne system show the potential and limitations for a practical application of the presented method.

Robust moving targets detection and velocity estimation using multi-channel and multi-look SAR images

For the sake of the robustness to ground moving targets indication (GMTI) for synthetic aperture radar (SAR) in heterogeneous environment, a new approach using multi-channel and multi-look SAR images is presented. This approach has been shown to attain improved suppression of monostatic clutter over its counterpart ( the multi-channel and single-look algorithm). In this approach, the joint data vector is utilized which has robustness to image coregistration and deleterious system errors in practice. Consequently, a series of radial velocity estimation approaches are presented. The estimation accuracy can be improved by the multi-look algorithms. Experimental airborne SAR data illustrates the effectiveness of the proposed GMTI schemes. It is shown that, compared with conventional single-look algorithms, the multi-look approach can obtain better clutter cancellation performance and more accurate velocity estimation.

Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion

In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from (multichannel) SAR images acquired over the same geographic region in different polarizations or at different frequencies at different times. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. Each SAR channel is modeled as a distinct information source, and a Markovian approach to data fusion is adopted. A Markov random field model is introduced that combines together the information conveyed by each SAR channel and the spatial contextual information concerning the correlation among neighboring pixels and formulated by using ldquoenergy functions.rdquo In order to address the task of the estimation of the model parameters, the expectation-maximization algorithm is combined with the recently proposed ldquomethod of log-cumulants.rdquo The proposed technique was experimentally validated with semisimulated multipolarization and multifrequency data and with real SIR-C/XSAR images.