Spatially Variant Filtering Network Based on Generalized Optimal Constraints for Sidelobe Suppression in SAR Images

Sidelobes commonly disturb synthetic aperture radar (SAR) image understanding and interpretation. Traditional spatially variant filtering algorithms achieve a superior tradeoff between sidelobe suppression and resolution preservation by means of adaptively calculating filtering parameters under some specific restrictions, such as filter design restriction and minimum amplitude constraint (MAC). These restriction aims to obtain an efficient analytical solution for filters, which is easy to calculate under unsupervised conditions. However, the restriction scope is so narrow that the suppression performance achieved by these filters is limited. Also, since the unsupervised optimization based on MAC indiscriminately minimizes amplitude, the main-lobe loss is unavoidable. To further improve the performance, a spatially variant convolution neural network (SVNN) is proposed, which consists of two core modules. One is the spatially variant filter generation (SVFG) module, adaptively generating superior spatially variant filters under more relaxed restrictions. The other is a paralleled shifted convolution (PSC) module, converting the signal format to achieve a fast and parallel spatially variant filtering process. Benefiting from more relaxed filter restrictions, the novel network successfully achieves better performance on sidelobe suppression. In addition, with supervised optimization based on another more accurate restriction, namely, minimum error constraint (MEC), the proposed algorithm also achieves superior main-lobe maintenance. All of them are validated by comparative experiments based on satellite data from GaoFen-3 and TerraSAR-X, and our proposed method achieves state-of-the-art performance. The entire project is available at https://github.com/suoyuxi/SVNN .