复数兼容全变分SAR目标结构特征增强

针对合成孔径雷达(synthetic aperture radar, SAR)成像目标复杂结构特征难以精准提取的问题, 设计复数兼容的多通道结构张量全变分(structure tensor total variation, STV)正则先验表征函数, 进而提出面向SAR目标结构特征增强的复数兼容-STV(complex value compatible-STV, CV-STV)优化算法。所提算法的结构先验函数设计涵盖实部/虚部两个通道的结构张量, 能适应SAR复成像数据特征并解析推导得到其近端算子，进而简化求解问题的模型复杂度。同时, 将CV-STV正则优化算法引入稀疏驱动先验, 借助交替方向多乘子法(alternating direction method of multipliers, ADMM)多任务优化框架实现目标散射点多特征的联合表征与增强。实验部分分别应用SAR仿真与实测数据对所提CV-STV正则优化算法进行有效性验证; 同时利用相变分析实验对比传统特征增强算法, 验证了所提算法的优越性。

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

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.