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 proposed an azimuth-range decouple-based L1 regularization method for wide ScanSAR imaging via extended chirp scaling (ECS) and applied it to the TerraSAR-X data to achieve large-scale sparse reconstruction. Compared with ECS, the conventional ScanSAR imaging algorithm based on matched filtering, the proposed method can improve the synthetic aperture radar image performance with full-sampling raw data for not only sparse but also nonsparse surveillance regions. It can also achieve high-resolution imaging for sparse considered scenes efficiently using down-sampling raw data. Compared with a typical L1 regularization imaging approach, which requires transfer of the two-dimensional (2-D) echo data into a vector and reconstruction of the scene via 2-D matrix operation, our proposed method has less computational cost and hence makes the large-scale regularization reconstruction of considered area become possible. The experimental results via real data validate the effectiveness of the proposed method.
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.
The elevation image quality of tomographic synthetic aperture radar (TomoSAR) data depends mainly on the elevation aperture size, number of baselines, and baseline distribution. In TomoSAR, due to the restricted number of baselines with irregular distributions, the elevation imaging quality is always unacceptable using the conventional spectral analysis approach. Therefore, for a given limited number of irregular baselines, the completion of data for the unobserved virtual uniform baseline distribution should be addressed to improve the spectral analysis-based TomoSAR reconstruction quality. We propose an Lq(0
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.
Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) technique has been demonstrated its success in the
estimation of forest height. Terrain slope is a factor that always affects the estimation accuracy. In this paper, the analysis of terrain influences was carried out in view of polarimetric orientation and local incidence angle shift, respectively. For the former, the relation between forest height estimation error and polarimetric orientation shift was derived by data simulation approach. For the latter, an analytical equation was derived by the theoretical analysis to describe the relation between true and estimated forest height. Then, possible methods for correcting terrain influences were presented, which including: 1) Design airborne experiment flight track along mountain ridge. 2) Utilize Pol-InSAR optimal coherences for forest height inversion if the computational efficiency is not an issue. 3) Revise the estimated forest height from RVoG model inversion, where the range slope can be calculated by InSAR dataset or a priori DEM.
Recent theory of compressed sensing (CS) has been widely used in many application areas. In this paper, we mainly
concentrate on the CS in radar and analyze the distinguishing ability of CS radar image based on information theory
model. The information content contained in the CS radar echoes is analyzed by simplifying the information
transmission channel as a parallel Gaussian channel, and the relationship among the signal-to-noise ratio (SNR) of the
echo signal, the number of required samples, the length of the sparse targets and the distinguishing level of the radar
image is gotten. Based on this result, we introduced the distinguishing ability of the CS radar image and some of its
properties are also gotten. Real IECAS advanced scanning two-dimensional railway observation (ASTRO) data
experiment demonstrates our conclusions.
Near range microwave imaging systems have broad application prospects in the field of concealed weapon
detection, biomedical imaging, nondestructive testing, etc. In this paper, the techniques of MIMO and sparse line array
are applied to near range microwave imaging, which can greatly reduce the complexity of imaging systems. In detail, the
paper establishes two-dimensional near range MIMO imaging geometry and corresponding echo model, where the
imaging geometry is formed by arranging sparse antenna array in azimuth direction and transmitting broadband signals
in range direction; then, by analyzing the relationship between MIMO and convolution principle, the paper develops a
method of arranging sparse line array which can be equivalent to a full array; and the paper deduces the backprojection
algorithm applied to near ranging MIMO imaging geometry; finally, the imaging geometry and corresponding imaging
algorithm proposed in this paper are investigated and verified by means of theoretical analysis and numerical simulations.
A novel approach is proposed to extract the tree crown from remote sensing image.The method is based on
Reversible Jump Markov Chain Monte Carlo sampler(RJMCMC), and improved data term is developd to describe the
tree crown, and jump and diffusion strategy of sampling is employed to optimize the energy function. Similar or better
extracting result is achieved with great efficiency , and the pre-segmentation is not need. The mothod is verified on
remote sensing images
Within a Bayesian framework, Brady proposed the adaptive texture approach for more accurate description and applied
this model in texture segmentation with a neighbourhood-based algorithm. In this paper, the efficiency of the texture
model in Brady's segmentation method is investigated. In the segmentation experiments of Brodatz texture mosaics and a
remote sensing image, the results show that the good segmentation performance mainly owes to the
neighbourhood-based algorithm, but not Brady's texture description model. Moreover, this probabilistic model is applied
in texture classification with a MAP method. To improve the correct classification rate of the image bank, a method
combining the best adaptive texture description of each class is proposed and obviously improves the rate from 91% to
95%.
Compared with unimodal wavelet packet subbands, multimodal subbands have strong texture discriminatory power. The
existence of mulitimodal subbands in dual-tree complex wavelet packet transform is proved. Similar to the multimodal
subbands in real wavelet packet transform, there are shift-modal subbands in complex transform to capture the
periodicities running through the texture images. Furthermore, the stability of multimodal subbands in real transform is
investigated through a classification experiment. It is concluded that, to the textures with small and very regular
periodicities, stable multimodal subbands can be obtained.
In order to overcome the azimuth spectrum folding occurrence in the spotlight imaging mode of the high resolution
spaceborne Synthetic Aperture Radar (SAR), we presented a new subaperture chirp scaling algorithm (CSA) in this
paper. Several phase functions of the extended CSA were modified according to the properties of subaperture processing.
These modifications make the new algorithm realize the precise recombination of the phase history data of each
subaperture without any additional computation load, making the subaperture overlapping and azimuth time extension
unnecessary. The validity of this new algorithm is proved through point target simulations by adopting TerraSAR-X
system parameters in the spotlight mode with 1m resolution and a swath of 10Km both in the range and azimuth.
Frequency scaling approach is a new spotlight SAR image formation algorithm. It precisely performs the range cell migration correction for dechirped raw data without interpolation by using a novel frequency scaling operation while residual video phase is corrected simultaneously. The computation requirements are lower than the other spotlight SAR image formation approaches such as polar format algorithm and range migration algorithm. In this paper, frequency scaling algorithm is applied to process high squint spotlight data. The new squint illumination geometry is defined and some modifications to the basic algorithm are presented. Point target simulations up to 45 deg squint angle are carried out to show the validity of the algorithm.
A SiC particle reinforced aluminum matrix composite (SiCp/Al composite) is melted by a high power continuous wave CO2 laser. Microstructure of the laser melted SiCp/Al MMC is studied as functions of laser processing parameters. The silicon carbide particles are completely dissolved during laser surface melting and aluminum silicon carbide Al4SiC4, aluminum carbide Al4C3, and primary silicon particles are produced in the laser melted surface. The size and volume fraction of the newly formed phases depend on the processing conditions. The relative volume fraction of Al4SiC4 to Al4C3 is strongly dependent on the laser linear energy density, a higher linear energy density leading to more Al4SiC4 formation. Result of anodic polarization corrosion test indicates that the corrosion resistance of the SiCp/Al MMC is not improved after laser surface melting.
An efficient tomographic algorithm is studied according to the high resolution imaging capability on small target terrain during the spotlight mode SAR procedure in this paper. By simplifying the procedure of Fourier reconstruction, reducing the A/D rat and PRF, implementing initial phase compensation, facilitating the hardware materialization, this tomographic algorithm for spotlight mode SAR greatly benefits the imaging processing efficiency. Necessary explanations, deductions, figures, tables and simulation results are given out.
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