基于梯度融合的极化SAR图像引导滤波

doi:10.12305/j.issn.1001-506X.2025.09.11

Abstract

Abstract:

In the guided filtering denoising algorithms for polarimetric synthetic aperture radar （SAR）, common methods typically introduce nonlinear kernel functions while neglecting the construction method of optimizing guidance image. In regard to this, a gradient fusion-based polarimetric SAR guided filtering algorithm is proposed for ship data denoising. It compares the different gradient computation methods and utilizes an optimized likelihood ratio gradient to obtain ship edge gradient images. By employing image binarization and morphological operations, the fused image of gradient information and intensity information is acquired and used as the guidance image to perform guided filtering on the original polarimetric SAR data. Multiple SAR images guided filtering denoising experiment demonstrate that the proposed algorithm can address the poor denoising performance of nonlinear kernel functions in existing guided filtering methods. Both visual results and numerical metrics indicate that the proposed algorithm outperforms the improved Lee filter and nonlinear kernel function guided filtering algorithms.

Key words: polarimetric synthetic aperture radar （SAR）, guided filtering, likelihood ratio gradient, information fusion

CLC Number:

TN 958

Xiang LI, Ding ZENG, Junjun YIN, Xianyu GUO, Jian YANG. Guided filtering for polarimetric SAR image based on gradient fusion[J]. Systems Engineering and Electronics, 2025, 47(9): 2890-2904.

Figures/Tables 16

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滤波类型	实验1			实验2
滤波类型	ENL	标准差	SSIM	ENL	标准差	SSIM
原始数据	1.1191	0.0772	1	1.2966	0.0777	1
改进Lee滤波	9.6233	0.0286	0.6088	9.2164	0.0292	0.5826
PNGF滤波	11.0304	0.0281	0.5660	10.4958	0.0279	0.5136
所提方法滤波	14.9783	0.0242	0.6436	13.3336	0.0251	0.6240

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Guided filtering for polarimetric SAR image based on gradient fusion

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