Fuzzy Clustering Based on Water Wave Optimization

Ren, Zifei; Wang, Chunzhi; Fan, Xinkai; Ye, Zhiwei

doi:10.1109/iccse.2018.8468778

Cited by 2 publications

(1 citation statement)

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“…It has the advantages of concise principle, fewer control parameters, and easy implementation. At present, 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.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Attribute Scattering Center Based on Water Wave Optimization Algorithm

Zhou

2021

Scientific Programming

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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.

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