Zhenfang Li

Generation of wide-swath and high-resolution SAR images from multichannel small spaceborne SAR systems

Future spaceborne synthetic aperture radar (SAR) systems will be required to produce high-resolution imagery over a wide area of surveillance. However, the minimum antenna area constraint makes it a contradiction to simultaneously obtain both unambiguous wide-area and high azimuth resolution. To overcome this limitation, a technique has been suggested that combines a broad illumination source with multiple receiving channels. Then, the coherent combination of the recorded multichannel signals will allow for the unambiguous SAR mapping of a wide ground area with fine azimuth resolution. This letter first gives an overview of current research work carried out about the generation of wide-swath and high-resolution SAR images from multichannel small spaceborne SAR systems, and then a space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is presented, which could be of help to overcome the existing difficulties in data processing. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing affect caused by the lower pulse repetition frequency and thereby retrieve the unambiguous azimuth wide (full) spectrum signal from the received signal. Following this operation, conventional SAR data processing tools can be applied to fully focus the SAR images. The performance of the approach is also discussed in this letter. The approach has the advantages of simplicity, robustness, and high efficiency.

Performance improvement for constellation SAR using signal processing techniques

A new concept of spaceborne synthetic aperture radar (SAR) implementation has recently been proposed - the constellation of small spaceborne SAR systems. In this implementation, several formation-flying small satellites cooperate to perform multiple space missions. We investigate the possibility to produce high-resolution wide-area SAR images and fine ground moving-target indicator (GMTI) performance with constellation of small spaceborne SAR systems. In particular, we focus on the problems introduced by this particular SAR system, such as Doppler ambiguities, high sparseness of the satellite array, and array element errors. A space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is proposed which can solve these problems to some extent. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing effect caused by the lower pulse-repetition frequency (PRF) and thereby retrieve the unambiguous azimuth wide (full) spectrum signals from the received echoes. Following this operation, conventional SAR data processing tools can be applied to focus the SAR images fully. The proposed approach can simultaneously achieve both high-resolution SAR mapping of wide ground scenes and GMTI with high efficiency. To obtain array element errors, an array auto-calibration technique is proposed to estimate them based on the angular and Doppler ambiguity analysis of the clutter echo. The optimizing of satellite formations is also analyzed, and a platform velocity/PRF criterion for array configurations is presented. An approach is given to make it possible that almost any given sparse array configuration can satisfy the criterion by slightly adjusting the PRF. Simulated results are presented to verify the effectiveness of the proposed approaches.

Image autocoregistration and InSAR interferogram estimation using joint subspace projection

In this paper, we propose a new method to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. The method takes advantage of the coherence information of neighboring pixel pairs to automatically coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can automatically coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) as the coregistration error reaches one pixel. The effectiveness of the method is also verified with the real data from the Spaceborne Imaging Radar-C/X Band SAR and the European Remote Sensing 1 and 2 satellites.

A Joint Image Coregistration, Phase Noise Suppression, and Phase Unwrapping Method Based on Subspace Projection for Multibaseline InSAR Systems

As is well known, image coregistration, interferometric phase noise suppression, and phase unwrapping are three key processing procedures of synthetic aperture radar interferometry (InSAR). The three procedures are cascaded in the conventional processing flow of InSAR. Unlike the conventional processing flow, in this paper we propose a joint processing idea to carry out image coregistration, interferometric phase noise filtering, and phase unwrapping simultaneously based on subspace projection for multibaseline InSAR systems. The joint processing method can perform the fine coregistration of all SAR images implicitly by extracting the correlation information in the neighboring pixel sets, suppress the phase noise by utilizing the orthogonality of the signal subspace and the corresponding noise subspace, and optimally estimate the unwrapped interferometric phases (or the terrain heights) by combining the pixel coherence and the baseline diversity of a multibaseline InSAR system. Simulated results are presented to verify the effectiveness of the joint processing method

Channel Error Estimation Methods for Multichannel SAR Systems in Azimuth

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar (SAR) systems are promising in high-resolution wide-swath imaging. However, the mismatch among channels will degrade the performance of DBF. In this letter, two novel methods are proposed to estimate channel errors for multichannel SAR systems in azimuth. The first method is based on the fact that the space spanned by the signal eigenvectors is equal to that spanned by the practical steering vectors. In the second method, the channel errors are directly estimated by the antenna patterns without matrix decomposition and inversion processing. Both the theoretical analysis and experiments demonstrate the effectiveness and efficiency of these two methods.

A New Strategy to Estimate Local Fringe Frequencies for InSAR Phase Noise Reduction

A new approach is presented to estimate the fringe frequencies in interferometric synthetic aperture radar (InSAR) phase image. A first-order model is usually used for fringe frequency estimation, but in steep regions or low-correlation regions, it often fails. In this letter, a prefiltered interferogram, obtained by the slope-compensated or conventional mean filter, is divided into small patches and unwrapped separately. Subsequently, we differentiate the local-phase-unwrapping results to obtain the fringe frequencies. Furthermore, the invalid fringe frequencies are eliminated by a statistical threshold. Finally, the interferogram is filtered by compensating the estimated fringe frequencies in the averaging window of the mean filter. The proposed method can obtain continuous fringe frequency estimation, and it is not constrained by the first-order model. The effectiveness of the proposed approach is verified by the simulated and real InSAR data.

Performance Analysis for Multichannel HRWS SAR Systems Based on STAP Approach

Incorporated with digital beam-forming processing, multichannel spaceborne synthetic aperture radar (SAR) systems are able to overcome the minimum antenna area constraint and yield high resolution and wide swath (HRWS) images. This letter mainly investigates the performance of the space-time adaptive processing (STAP) approach applied to HRWS SAR imaging. The analytic expressions for the signal-to-noise ratio (SNR) scaling factor and azimuth ambiguity to signal ratio (AASR) are derived and confirmed by the simulated results. Then, the influence of channel errors on HRWS imaging is analyzed in detail.

Residues Cluster-Based Segmentation and Outlier-Detection Method for Large-Scale Phase Unwrapping

2-D phase unwrapping is an important technique in many applications. However, with the growth of image scale, how to tile and splice the image effectively has become a new challenge. In this paper, the phase unwrapping problem is abstracted as solving a large-scale system of inconsistent linear equations. With the difficulties of large-scale phase unwrapping analyzed, L0-norm criterion is found to have potentials in efficient image tiling and splicing. Making use of the clustering characteristic of residue distribution, a tiling strategy is proposed for L0-norm criterion. Unfortunately, L0-norm is an NP-hard problem, which is very difficult to find an exact solution in a polynomial time. In order to effectively solve this problem, equations corresponding to branch cuts of L0-norm in the inconsistent equation system mentioned earlier are considered as outliers, and then an outlier-detection-based phase unwrapping method is proposed. Through this method, a highly accurate approximate solution to this NP-hard problem is achieved. A set of experimental results shows that the proposed approach can avoid the inconsistency between local and global phase unwrapping solutions caused by image tiling.

An Adaptively Weighted Least Square Estimation Method of Channel Mismatches in Phase for Multichannel SAR Systems in Azimuth

Multichannel synthetic aperture radar (SAR) systems in azimuth can achieve high-resolution and wide-swath imaging. However, the quality of final SAR image can be degraded by the channel mismatch in phase which increases the energy outside the processed Doppler bandwidth (PDB). To address this problem, a calibration algorithm is proposed in this letter by minimizing the energy outside the PDB. Theoretical analysis shows that the presented method can be interpreted as an adaptively weighted least square estimation problem, where the weights are related to the signal-to-noise ratio (SNR) of the echoes from different directions. Simulation results reveal that our method outperforms the conventional methods in the case of quasi-uniform sampling, particularly at the low-SNR region.

Multi-Channel SAR-GMTI Method Robust to Coregistration Error of SAR Images

High accuracy in complex images coregistration is essential in ground moving target indication (GMTI) processing of synthetic aperture radar (SAR) data, normally termed SAR-GMTI. The clutter suppression performance is proportional to the coregistration accuracy. In this correspondence, we propose a new SAR-GMTI approach, which is robust to the SAR images coregistration error. The observed clutter-plus-noise vector is built using the neighboring pixels as the first step, thus the corresponding covariance matrix, termed as joint covariance matrix, can be estimated. The joint noise subspace is obtained by eigen-decomposing of the joint covariance matrix. The clutter is suppressed successfully by a projecting operation, i.e., a projection of the joint observed vector into the resulting joint noise subspace. The information of neighboring pixels is substantially exploited in the clutter suppression, resulting in the robustness, even at the presence of large coregistration error. Both the simulated and real multi-channel airborne data are used to validate the proposed approach.