复数兼容全变分SAR目标结构特征增强

doi:10.12305/j.issn.1001-506X.2022.06.12

Abstract

Abstract:

Aiming at the problem that it is difficult to accurately extract complicated structure features of synthetic aperture radar (SAR) imaging targets, a complex compatible multi-channel structure tensor total variation (STV) regularizer is designed, and then a complex value compatible-STV (CV-STV) optimization algorithm for SAR target structural feature enhancement is proposed. The structural feature prior designs multi-channel structural tensor, which can adapt to the characteristic of SAR complex imaging data. Specially, the proximal operator of structural feature prior is derived analytically, to simplify the model complexity of the problem to be solved. At the same time, the sparsity-driven prior is introduced into the CV-STV optimization algorithm, the cooperative representation and enhancement of the multi-features for target scatterers can be realized by the framework of ADMM. In the experimental part, the effectiveness of the proposed CV-STV optimization algorithm is verified by SAR simulated and raw data, respectively. Meanwhile, the phase transition analysis experiment is utilized to compare with the conventional feature enhancement algorithms to verify the superiority of the proposed algorithm.

Key words: synthetic aperture radar (SAR), structure tensor total variation (STV), complex value compatibility, cooperative optimization

CLC Number:

TN95

Minghui GAI, Su ZHANG, Weitian SUN, Yude NI, Lei YANG. Structural-feature enhancement of SAR targets based on complex value compatible total variation[J]. Systems Engineering and Electronics, 2022, 44(6): 1862-1872.

Figures/Tables 9

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Structural-feature enhancement of SAR targets based on complex value compatible total variation

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