scholarly journals Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

2019 ◽  
Vol 9 (13) ◽  
pp. 2728 ◽  
Author(s):  
Rongchun Hu ◽  
Zhenming Peng ◽  
Kelong Zheng
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Transformation Function ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Imaging Result ◽  
Target Imaging ◽  
Sar Imaging ◽  
Line Target ◽  
Subsequent Target

This paper presents a modulus stretch-based circular Synthetic Aperture Radar (SAR) imaging method. This method improves the traditional backprojection algorithm for circular SAR imaging, and introduces the modulus stretch transformation function in the imaging process. By performing a modulus stretch transformation on the intermediate results, the target contour in the final imaging result is thinner and clearer. A thinner and clearer contour can help to increase the recognizability of the target and provide a basis for subsequent target recognition. The proposed method is demonstrated on the line target imaging simulations and Gothca dataset.

scholarly journals A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4133 ◽  
Author(s):  
Bing Sun ◽  
Chuying Fang ◽  
Hailun Xu ◽  
Anqi Gao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Sar Image ◽  
Sar Images ◽  
Gradient Operator ◽  
Sar Imaging ◽  
Aperture Radar

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

scholarly journals Sub-Aperture Partitioning Method for Three-Dimensional Wide-Angle Synthetic Aperture Radar Imaging with Non-Uniform Sampling

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 629 ◽  
Author(s):  
Dou Sun ◽  
Shiqi Xing ◽  
Yongzhen Li ◽  
Bo Pang ◽  
Xuesong Wang
Keyword(s):  
Synthetic Aperture Radar ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Wide Angle ◽  
Uniform Sampling ◽  
Equal Interval ◽  
Partitioning Methods ◽  
Imaging Result ◽  
Sar Imaging ◽  
Non Uniform Sampling

For a three-dimensional wide-angle synthetic aperture radar (SAR) with non-uniform sampling, it is necessary to divide its large aperture into several small sub-apertures before imaging due to the anisotropic characteristics of the target. The existing sub-aperture partitioning methods divide the aperture with equal intervals. However, for the non-uniformly sampled SAR, those equal-interval partitioning methods may have a bad effect on the resolution of the SAR imaging result. In view of this, a sub-aperture partitioning method for three-dimensional wide-angle SAR imaging with non-uniform sampling was proposed in this paper. First, we analyzed the relationship between the three-dimensional resolution and the sampling distribution in K-space based on the Cramer–Rao lower bound. Subsequently, according to the distribution of K-space sampling, the optimum size of each sub-aperture was found and the aperture was divided non-uniformly. Furthermore, the proposed method was validated by electromagnetic simulation data. The proposed sub-aperture partitioning method ensured that the resolution of each sub-aperture was high and consistent. By comparing with the equal-interval partitioning method, the experimental results showed that our proposed method had a higher resolution imaging result.

scholarly journals A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1252 ◽  
Author(s):  
Rongchun Hu ◽  
Zhenming Peng ◽  
Juan Ma
Keyword(s):  
Synthetic Aperture Radar ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Synthetic Aperture ◽  
Speckle Reduction ◽  
Wide Angle ◽  
Target Imaging ◽  
Standard Orientation ◽  
Aperture Radar

Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.

scholarly journals A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

2016 ◽  
Vol 3 (11) ◽  
pp. 446-462 ◽  
Author(s):  
H. Vickers ◽  
M. Eckerstorfer ◽  
E. Malnes ◽  
Y. Larsen ◽  
H. Hindberg
Keyword(s):  
Synthetic Aperture Radar ◽  
Snow Avalanche ◽  
Synthetic Aperture ◽  
Sar Imaging ◽  
Aperture Radar
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