Voxel area sculpturing‐based 3D scene reconstruction from single‐pass CSAR data

Circular synthetic aperture radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. However, the CSAR image focusing is affected by the motion deviations of platform. In the processing of experimental CSAR data to deal with motion errors, the main way is using setup calibrators, which restricts its widespread applications. In this paper, based on the estimation of motion errors, an autofocus CSAR imaging strategy is proposed without using any setup calibrator. The first step is to split the entire aperture into several subapertures, the second step is to process the data in subapertures with an autofocus backprojection method, and the last step is to obtain the final CSAR image by merging the subimages obtained from the subaperture processing. The CSAR data processing results prove that the proposed strategy can remove the motion errors accurately and acquire well-focused CSAR images.

In conventional synthetic aperture radar (SAR) working mode, targets are assumed isotropic due to the limited aperture length. However, most of man-made targets are anisotropic. Therefore, the anisotropic scattering can help us do man-made target detection. Circular SAR (CSAR) [1] is a new SAR working mode and it can obtain the anisotropic scattering of the target by 360° observation. In this paper, the multi-angular statistical properties of targets are analyzed. The probability density functions (PDF) of the anisotropic target are various under different aspect viewing angles, while the PDFs of the isotropic target are basically stable. Then a man-made target detection method is proposed based on the multi-angular statistical property. Likelihood ratio test [2] is used to judge whether the statistical property of scattering is anisotropic or isotropic. Then anisotropic scatterings, which represent the man-made targets, can be discriminated from isotropic scatterings by thresholding. An X-band chamber circular SAR data and a C-band airborne circular SAR data are used to illustrated our idea.

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method. 1

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.

Circular synthetic aperture radar (CSAR) is a high resolution 2D and 3D imaging mode, to obtain the demand of all observation information. Circular motion trajectory make motion compensation problem is very difficult. However, existing method, such as autofocus back projection (ABP) algorithm, cannot effectively solve the problem of motion compensation in CSAR. In this paper, an autofocus algorithm based on minimum entropy is used to solve this problem. It can accurately estimate phase errors in CSAR. The simulated CSAR data are used to validate performance of minimum entropy autofocus (MEA) algorithm.

A recent circular synthetic aperture radar data collection contained various vehicles and calibration targets placed throughout a 5 km scene. By observing multiple orbits of the radar, the down-range distance measurements to scattering features show noticeable drift on the order of 2 m from orbit to orbit. The large scene contained 14 quad-trihedral (QT) calibration targets with radar cross sections that are similar to point targets in the elevation range of the scene. This paper presents an algorithm that uses the QTs to generate global range focusing parameters. Qualitative and quantitative results show the focusing provides a significant improvement to vehicle target discrimination.

A three dimensional (3D) imaging method which is a complex-valued generalized Radon transform (CGRT)-CLEAN algorithm for circular synthetic aperture radar (CSAR) is proposed in this chapter. Based on the analysis of CSAR echo model, the echo of a scatterer in the range-azimuth domain can be modeled as a single cycle sinusoid. Inspired by the generalized Radon transform, this chapter utilizes the CGRT to estimate for scatterers’ positions. Then, combining the CGRT with CLEAN technique, the parameters of a scatterer which include position information and reflection coefficient can be obtained. Thus, a 3D image of a target can also be reconstructed. The performances of the CGRT-CLEAN method have been verified by simulations, and the results confirm the effectiveness of the proposed algorithm for processing CSAR data.

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual-channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target’s range history for airborne dual-channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2-D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target’s motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm.

As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.

Circular synthetic aperture radar (CSAR) enables 360-degree observation of the scene. Compared with coherent imaging, noncoherent imaging can suppress speckle and obtain a large number of texture features. However, due to the existence of a man-made anisotropic targets and the unique imaging geometry of CSAR, the different subaperture images are quite different, which makes the registration very difficult. Therefore, this manuscript proposed a factorized registration strategy. First, the full aperture is divided into multiple subapertures; then the adjacent subaperture images are registered and the noncoherent superimposition of the subaperture images yields the next stage image; finally, the previous step is performed iteratively until the final imaging result is obtained. Experimental results based on Ka-band CSAR data verify the effectiveness of the strategy.

An airborne circular synthetic aperture radar system captured data for a 5 km diameter area over 31 orbits. For this challenge problem, the phase history for 56 targets was extracted from the larger data set and placed on a DVD for public release. The targets include 33 civilian vehicles of which many are repeated models, facilitating training and classification experiments. The remaining targets include an open area and 22 reflectors for scattering and calibration research. The circular synthetic aperture radar provides 360 degrees of azimuth around each target. For increased elevation content, the collection contains two nine-orbit volumetric series, where the sensor reduces altitude between each orbit. Researchers are challenged to further the art of focusing, 3D imaging, and target discrimination for circular synthetic aperture radar.

Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.

In this paper, we propose possibility for reconstruction of surface of an underwater object or 3D scene reconstruction of an underwater environment using an economical RGB-D sensor such as Microsoft Kinect. Reconstructing the 3D surface of an underwater object is a challenging task due to degraded quality of underwater images. There are various reasons of quality degradation of underwater images i.e., non-uniform illumination of light on the surface of objects, scattering and absorption effects. Particles and impurities present in underwater produces Gaussian noise on the captured underwater optical images which degrades the quality of images. However, using depth sensors, as a cost effective alternative, we aim to show that underwater 3D scene reconstruction is possible with sight tradeoffs on accuracy but major cost saving. The acquired depth data is proposed to be processed by applying real-time mesh generating techniques from the acquired point cloud. The experimental result aims to show that the proposed method reconstructs 3D surface of underwater objects accurately using captured underwater depth images.

In conventional polarimetric synthetic aperture radar, targets are usually assumed isotropic, and potential polarimetric variations in azimuth are ignored. As to polarimetric circular SAR (CSAR), the azimuthal aperture is much larger, and polarimetric variations in azimuth are no longer negligible. Moreover, whether a target has changed polarimetric properties in azimuth, i.e., anisotropic, is an important feature. $H/\alpha $ classification scheme is a famous and effective unsupervised classification method. However, when applying $H/\alpha $ classification scheme against polarimetric CSAR data, problems occur. First, during the formation of a large aperture, polarimetric properties from different angles of view are combined, which affects the estimation of $H$ and $\alpha $ . Second, $H/\alpha $ method cannot distinguish anisotropic and isotropic targets. In this paper, a pixel-wise $H/\alpha $ calculation method and an unsupervised classification scheme against polarimetric CSAR images are proposed to solve the two problems. With the pixel-wise $H/\alpha $ calculation method, $H$ and $\alpha $ are more accurately calculated. Meanwhile, anisotropic and isotropic targets which have same scattering mechanism can be distinguished by the proposed classification method. The effectiveness of the pixel-wise $H/\alpha $ calculation method is demonstrated by both the simulated and real data. The unsupervised classification method is demonstrated based on real data, acquired by airborne CSAR system at $P$ -band, the Institute of Electronics, Chinese Academy of Sciences, Beijing, China.

This article presents a synthetic aperture radar (SAR) imaging algorithm based on a compressed sensing (CS) reconstruction approach, which is suitable for the detection of foreign object debris (FOD) located in a multilayered circular dielectric structure for nondestructive testing (NDT) application. The method proposed considers the refraction model at the boundaries between each two adjacent layers. This method is different from free space as imaging background assumption in previous CS-based SAR imaging applications. In addition, the prior information that the FOD is displayed with a sparse distribution is applied. Therefore, the method proposed could obtain better focus on effectiveness than time-reversal imaging algorithm with the same measurement points. Further, the CST simulations and the experiment were conducted by taking two small metallic targets as FOD embedded in the three-layered circular dielectric structure. The results of simulations and experiment demonstrated that the proposed method has better effect than previous methods.