As an efficient way to interpret the measurements of high-frequency synthetic aperture radar (SAR), an attributed scattering center (ASC) model provides concise and physically relevant features of complex targets. However, accurate extractions of ASCs have been heavily penalized by high memory requirements and computational complexity. We propose to convert SAR measurements to sparse representations in the image domain where the ASC model parameters can be estimated by using an orthogonal matching pursuit (OMP) algorithm or its Newtonlized variation. Two important new properties of the ASC model are unveiled in the image domain, namely, “translatability” and “additivity.” The properties can help save the dictionary of OMP from sampling the position and length parameters. The atoms of the dictionary become localized, thereby reducing the dictionary size and accelerating ASC extractions. Extensive experiments are conducted based on open-source XPATCH Backhoe data, measured MSTAR data, and synthetic backscatter data. The results show that the proposed approach is able to outperform existing image-domain algorithms in terms of accuracy and noise resistance, and outperform existing frequency-domain algorithms in terms of memory requirement and runtime.

中文翻译：

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基于图像域稀疏表示的高效属性散射中心提取


作为解释高频合成孔径雷达 (SAR) 测量结果的有效方法，归因散射中心 (ASC) 模型提供了复杂目标的简洁且物理相关的特征。然而，高内存要求和计算复杂性严重限制了 ASC 的准确提取。我们建议将 SAR 测量结果转换为图像域中的稀疏表示，其中可以使用正交匹配追踪 (OMP) 算法或其牛顿化变体来估计 ASC 模型参数。 ASC模型的两个重要新属性在图像领域被揭晓，即“可翻译性”和“可加性”。这些属性可以帮助保存来自采样位置和长度参数的 OMP 字典。字典的原子变得局部化，从而减小字典大小并加速 ASC 提取。基于开源 XPATCH Backhoe 数据、测量的 MSTAR 数据和合成反向散射数据进行了大量实验。结果表明，该方法在精度和抗噪性方面优于现有的图像域算法，在内存需求和运行时间方面优于现有的频域算法。