A Novel Method for Building Contour Extraction Based on CSAR Images

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) 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 this article, we propose a novel method of estimation of crack orientation on a metallic surface using linearly polarized circular synthetic aperture radar (SAR) imaging. We modeled a crack using a thin linear scatterer and considered the rotation of polarization basis vectors that were designed to match a predefined orientation vector corresponding to a temporary estimate of the orientation of the linear scatterer. The radar image intensity was expected to be a maximum when the specified orientation vector exactly matched the true scatterer orientation angle; therefore, to estimate the orientation of the scatterer, we aimed to obtain the maximum intensity of the image by varying the orientation vector. We validated the proposed scheme of estimation via a numerical electromagnetic simulation using the method of moments (MoM) and laboratory measurements carried out in an anechoic chamber. In this experiment, a metallic plate with slits was used as a simple surface crack model and the root mean square error and maximum error of the angular estimation were 1.9-degree and 5.1-degree, respectively.

In circular synthetic aperture radar (CSAR), radar collects data over a circular trajectory. This wide-angle non-linear collection geometry makes CSAR capable of obtaining both two-dimensional (2D) high resolution image and three-dimensional (3D) image. In this paper, compressive sensing technique is used in 3D CSAR imaging. Not only echo samples used in imaging are greatly reduced, but also the imaging results are with good performance. However, the consumed time of reconstruction is very long. In order to overcome this problem, graphic process unit (GPU) is furtherly used in 3D compressive sensing CSAR imaging in this paper. Simulation results show that GPU can improve the efficiency of the 3D reconstruction.

Circular synthetic aperture radar (CSAR) has a 360° observation capability on the central observation scenario. A typical way to process CSAR imaging is to cut data into small sub-apertures because most targets are only coherent at a very small observation angle. There are many sub-aperture imaging methods after development in recent years. The back-projection algorithm is widely used because it is simple and can be applied to an arbitrary trajectory. Because of the limitation of the Nyquist sampling frequency and influence of the antenna sidelobe, azimuth ambiguity is a phenomenon that may occur in the radar imaging process. The existing researches typically choose the back-projection (BP) imaging area according to the SAR platform flight path and the antenna beam width. The limitation of the CSAR imaging area and its azimuth ambiguity region are rarely analyzed theoretically. This paper focus on the sub-aperture imaging of CSAR, based on the BP algorithm, which derives the relationship of azimuth ambiguity with CSAR parameters such as the pause repeat frequency (PRF), slant range angle, velocity of radar platform, etc. This paper proposes an equation for the calculation of the azimuth ambiguity region and analyzes the limitations, which provides theoretical support for CSAR parameter design, imaging area selection, and azimuth ambiguity analysis.

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.

Circular synthetic aperture radar (SAR) imaging on the geosynchronous orbit has significant potential advantages, such as high resolution three-dimensional imaging, large coverage, but it also has challenges to overcome. One major problem limiting the high-resolution geosynchronous circular SAR (GEOCSAR) performances is the atmospheric effects including the tropospheric and ionospheric effects. In this study, the authors mainly consider the tropospheric effects on L-band GEOCSAR imaging. Since the integration time of GEOCSAR may be as long as 24 h, and the coverage can reach 1/3 of the Earth's surface, the effects of the spatial and temporal variation of the tropospheric refractive index cannot be ignored if well focused images are to be obtained. The analysis shows that the temporal variation of the tropospheric refractive index may cause severe degradation of the SAR images; the spatial variation of the refractive index and the tropospheric turbulence are not the important factor limiting the GEOCSAR imaging performance.

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Recently, detecting the interesting target accurately in circular synthetic aperture radar (CSAR) images has become one of the most important research directions in the actual CSAR application. Due to the influence of the data acquisition mode, the size of the CSAR image is usually large. When the vehicle targets in the CSAR image are parked tightly, they usually cannot be detected well using the traditional detection methods. Therefore, a vehicle target detection method based on YOLOv5 in the CSAR image is proposed in this paper. Firstly, the image augmentation technique is used to solve the problem of the insufficient training samples, and then the data set is input to the model for training. Secondly, the test image is segmented, and then the trained model is used to detect the vehicle target respectively. Finally, the post-processing is used to optimize the detection results. The CSAR image based on the Gotcha dataset is used to verify the correctness of theoretical analysis and feasibility of the proposed method.

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.

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.

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.

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.