Abstract
For the problem of attribute scattering center parameter estimation in synthetic aperture radar (SAR) image, a method based on the water wave optimization (WWO) algorithm is proposed. First, the segmentation and decoupling of high-energy regions in SAR image are performed in the image domain to obtain the representation of a single scattering center. Afterwards, based on the parameterized model of the attribute scattering center, an optimization problem is constructed to search for the optimal parameters of the separated single scattering center. In this phase, the WWO algorithm is introduced to optimize the parameters. The algorithm has powerfully global and local searching capabilities and avoids falling into local optimum while ensuring the optimization accuracy. Therefore, the WWO algorithm could ensure the reliability of scattering center parameter estimation. The single scattering center after solution is eliminated from the original image and the residual image is segmented into high-energy regions, so the parameters of the next scattering center are estimated sequentially. Finally, the parameter set of all scattering centers in the input SAR image can be obtained. In the experiments, firstly, the parameter estimation verification is performed based on the SAR images in the MSTAR dataset. The comparison of the parameter estimation results with the original image and the reconstruction based on the estimated parameter set reflect the effectiveness of the proposed method. In addition, the experiment is also conducted using the SAR target recognition algorithms based on the estimated attribute parameters. By comparing the recognition performance with other parameter estimation algorithms under the same conditions, the performance superiority of the proposed method in attribute scattering center parameter estimation is further demonstrated.
Highlights
Synthetic aperture radar (SAR) has all-day and all-weather high-resolution imaging capabilities, which is widely used in military and civilian fields [1,2,3,4]
According to the theory of compressive sensing, the researchers designed the attribute scattering center estimation algorithms based on sparse representation [17], assuming that the attribute scattering centers in a single SAR image are sparsely distributed in the entire parameter space. e sparse representation methods avoid the local decoupling in the image domain, but the consistency of the Scientific Programming estimated results with the original image is often difficult to guarantee
The input SAR image is analyzed, and the image segmentation algorithm is used to obtain the region with the largest energy as the image domain representation of a single scattering center. en, for the decoupled single scattering center data, the optimization is performed under the constraints of the attribute scattering center model to obtain the best attribute parameters
Summary
Synthetic aperture radar (SAR) has all-day and all-weather high-resolution imaging capabilities, which is widely used in military and civilian fields [1,2,3,4]. E sparse representation methods avoid the local decoupling in the image domain, but the consistency of the Scientific Programming estimated results with the original image is often difficult to guarantee In this paper, based on the traditional image domain attribute scattering center estimation methods, the water wave optimization (WWO) algorithm is introduced to realize the parameter optimization of a single scattering center. The WWO algorithm has been widely used and verified in the fields of task allocation, image processing, etc., with higher accuracy and robustness [19,20,21,22,23] For this reason, this paper introduces the WWO algorithm into the parameter estimation of the single attribute scattering center to obtain more reliable estimation results. The MSTAR dataset is used to verify the proposed method, including the contributions to the parameter estimation of SAR images and the target recognition based on attribute scattering center matching, respectively. e experimental results prove the effectiveness of the proposed method and its superiority compared with the existing methods
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