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摘要: 基于稀疏信号处理的合成孔径雷达(SAR)成像(稀疏SAR成像)是稀疏微波成像的一个重要研究方向，相较于经典SAR，稀疏SAR成像在提升成像性能等方面具有重要优势。然而，受困于较大计算代价，其难以用于大观测场景的稀疏恢复，这极大限制了其应用范围。此外，无论军用还是民用，各国星载SAR系统的技术性能指标均是保密的，因此相较于原始回波，通常的公开数据都是经匹配滤波算法重构的SAR复图像。因而如何基于复图像数据进行稀疏成像，对提升现有SAR图像质量、降低稀疏成像计算代价具有重要意义。高分三号是我国首颗1 m分辨率C波段多极化SAR卫星，它具有成像分辨率高、幅宽大等优势，对提升我国灾害监测、海洋监视等能力具有重要作用。该文将一种基于复图像数据的稀疏SAR成像技术引入到高分三号SAR复图像的性能提升当中。实验结果表明，经稀疏处理后的图像拥有更低的旁瓣、更高的信杂噪比以及更优的目标可分辨率能力。且类似于匹配滤波算法重建图像，稀疏恢复结果也可以很好地保持图像统计分布及相位信息，使得稀疏重构的高分三号SAR图像仍适用于干涉、恒虚警率检测等应用。Abstract: Sparse signal processing-based Synthetic Aperture Radar (SAR) imaging, also known as sparse SAR imaging, is the main research direction of sparse microwave imaging theory. Compared with a conventional SAR system, sparse SAR imaging radar has significant potential to improve imaging performance. However, because it requires heavy computations, the application of sparse SAR imaging in large-scene recovery has become difficult, which restricts its further applications. Additionally, complex SAR images, rather than raw data, are usually used for data archiving due to a number of reasons such as data copyright and system confidentiality. Therefore, it is worthwhile to study how sparse imaging can be achieved using only Matched Filtering (MF) recovered complex images with less computational cost. GaoFen-3 is China’s first 1-m resolution multi-polarization C-band satellite. It has a high-resolution, wide swath imaging ability and hence plays an important role in disaster monitoring and ocean surveillance applications. In this paper, we introduce a complex image-based sparse SAR imaging method to process GaoFen-3 complex image data and improve image performance. Experimental results show that the sparse imaging results have lower sidelobes, higher signal-to-clutter and noise ratio, and better target distinguishing ability compared with inputted images. Additionally, sparse imaging can effectively preserve the statistical distribution and phase information of images that makes the recovered GaoFen-3 sparse image-based applications such as interferometric synthetic aperture radar and constant false alarm ratio detection possible.
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Key words:
- Synthetic Aperture Radar (SAR) /
- Sparse imaging /
- GaoFen-3 /
- Regularization
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图 1 不同方法的海岸线区域重建结果
Figure 1. The reconstructed images of coastal area by different methods
图 2 不同方法的海面舰船重建结果
Figure 2. The reconstructed images of ships on the sea surface by different methods
图 4 稀疏SAR成像方法重构图像背景统计分布保持
Figure 4. Background distribution preservation of sparse SAR imaging method recovered image
图 5 稀疏SAR成像方法重构图像相位信息保持
Figure 5. Phase information preservation of sparse SAR imaging method recovered image
图 6 匹配滤波图像与本文稀疏SAR成像方法非稀疏解之间的相位差
Figure 6. Phase difference between the MF recovered image and the non-sparse solution of sparse SAR imaging method
表 1 不同方法重建结果的目标背景比TBR(dB)
Table 1. TBR values of the recovered images by different methods (dB)
区域/方法 匹配滤波重构图像 稀疏解 非稀疏解 区域1 30.2835 41.8379 41.8346 区域2 34.2496 44.3981 44.3956 区域3 39.9948 50.9537 50.9509 
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