Polarimetric synthetic aperture radar (PolSAR) obtains polarimetric scattering of targets. The scattering properties are usually considered as invariant in azimuth. In some new SAR mode, such as wide-angle SAR and circular SAR (CSAR), targets are illuminated for longer time and look angle changes a lot. Moreover some targets have different physical shape in different look angle. Thus scattering properties can no longer be considered as invariant in azimuth. Variations across azimuth should be considered as useful information and are important parts of targets’ scattering properties. In this paper, polarimetric data are cut into subapertures in order to achieve scattering properties in different look angle. Target vector and coherency matrix are de- fined for multi-aperture situation. Polarimetric entropy for multi-aperture situation is then defined and named with multi-aperture poalrimetric entropy(MAPE). MAPE is calculated based on eigenvalue of multi-aperture coherency matrix. MAPE describes variations of scattering properties across subapertures. When MAPE is low, scattering properties change a lot across subapertures, which refers to anisotropic targets. When MAPE is high, there are few variations across subapertures, which refers to isotropic targets. Thus anisotropic targets and isotropic targets can be identified by MAPE. The effectiveness of MAPE is demonstrated on polarimetric CSAR(Pol-CSAR) data, acquired by the Institute of Electronics airborne CSAR system at P-band.
This paper presents an interferometric synthetic aperture radar (InSAR) imaging method based on L1 regularization reconstruction model for SAR complex-image and raw data via complex approximated message passing (CAMP) with joint reconstruction model. As an iterative recovery algorithm for L1 regularization, CAMP can not only obtain the sparse estimation of considered scene as other regularization recovery algorithms, but also a non-sparse solution with preserved background information, thus can be used to InSAR processing. The contributions of the proposed method are as follows. On the one hand, as multiple SAR complex images are strongly correlated, single-channel independent reconstruction via Lq regularization cannot preserve the interferometric phase information, while the proposed mixed norm-based L1 regularization joint reconstruction model via CAMP algorithm can ensure the preservation of interferometric phase information among multiple channels. On the other hand, the interferogram reconstructed by the proposed CAMP-based InSAR imaging with joint reconstruction model can improve the performance of noise reduction efficiently compared with conventional matched filtering (MF) results. Experiments carried out on simulated and real data confirmed the feasibility of the L1 regularization joint reconstruction model via CAMP for InSAR processing with preserved interferometric phase information and better noise reduction performance.
In this paper, we develop a group sparsity based wide angle synthetic aperture radar (WASAR) imaging model and propose a novel algorithm called backprojection based group complex approximate message passing (GCAMP-BP) to recover the anisotropic scene. Compare to conventional backprojection based complex approximate message passing (CAMP-BP) algorithm for the recovery of isotropic scene, the proposed method accommodates aspect dependent scattering behavior better and can produce better imagery. Simulated and experimental results are presented to demonstrate the validity of the proposed algorithm.
KEYWORDS: 3D acquisition, Interferometry, Synthetic aperture radar, 3D image processing, 3D modeling, Scattering, Image processing, Data processing, Sensors, Image segmentation
Circular SAR has several attractive features, such as full-aspect observation, high resolution, and 3D target reconstruction capability, thus it has important potential in fine feature description of typical targets. However, the 3D reconstruction capability relies on the scattering persistence of the target. For target with a highly directive scattering property, the resolution in the direction perpendicular to the instantaneous slant plane is very low compared to the range and azimuth resolutions, and the 3D structure of target can hardly be obtained. In this paper, an Interferometric Circular SAR (InCSAR) method is proposed to reconstruct the full-aspect 3D structure of typical targets. InCSAR uses two sensors with a small incident angle difference to collect data in a circular trajectory. The method proposed in this paper calculates the interferometric phase difference (IPD) of the image pair at equally spaced height slices, and mask the original image with an IPD threshold. The main principle is that when a scatterer is imaged at a wrong height, the image pair has an offset, which results in a nonzero IPD, and only when the scatterer is correctly imaged at its true height, the IPD is near zero. The IPD threshold is used to retain scatterers that is correctly imaged at the right height, and meanwhile eliminate scatterers that is imaged at a wrong height, thus the 3D target structure can be retrieved. The proposed method is validated by real data processing, both the data collected in the microwave chamber and the GOTCHA airborne data.
KEYWORDS: 3D image processing, Synthetic aperture radar, Reconstruction algorithms, Radar imaging, Stereoscopy, Signal to noise ratio, Imaging systems, 3D acquisition, 3D modeling, Antennas
We propose an imaging algorithm for downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) in the circumstance of cross-track sparse and nonuniform array configuration. Considering the off-grid effect and the resolution improvement, the algorithm combines pseudo-polar formatting algorithm, reweighed atomic norm minimization (RANM), and a parametric relaxation-based cyclic approach (RELAX) to improve the imaging performance with a reduced number of array antennas. RANM is employed in the cross-track imaging after pseudo-polar formatting the DLSLA 3-D SAR echo signal, then the reconstructed results are refined by RELAX. By taking advantage of the reweighted scheme, RANM can improve the resolution of the atomic norm minimization, and outperforms discretized compressive sensing schemes that suffer from off-grid effect. The simulated and real data experiments of DLSLA 3-D SAR verify the performance of the proposed algorithm.
We propose a model for soil moisture change detection using phase information of synthetic aperture radar data. It is expected to be applied for drought monitoring over grasslands in north China. This model is developed from the coherent scattering model, which was originally studied for random oriented volume over ground scattering. Compared to the conventional water content estimation methods employing amplitude information, the methods based on phase information have advantages over change detection. In particular, the phases caused by topography can be removed by the use of external digital elevation model data with high accuracy. Simulations are presented to show the phase sensitivity on soil moisture variations, as well as soil moisture changes that can be feasibly inverted under different conditions of system phase accuracy and incidence angle. Then the inversion scheme is given on the basis of the proposed model. Finally, a relevant experiment in the anechoic chamber was implemented, in which good agreement is achieved between the model computations and the measurements. The results are discussed considering the practical limitations of potential applications.
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