Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images
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摘要: 稀疏微波成像利用观测场景在空时频极化等表示域上的稀疏先验,通过线性综合测量方式获得比传统Nyquist采样低得多的回波数据,使用优化重构算法恢复观测场景微波图像,相对于传统微波成像体制具有诸多优势。在稀疏微波成像体制下,图像的获取和表征均发生了变化,需要在雷达图像理解现有理论和方法的基础上,研究新的特征分析和认知解译理论与方法。该文分析了稀疏SAR图像的统计特性以及点、线、面等特征的变化情况,对于使用空域稀疏模型重构的SAR图像,统计分布退化,适当降低采样率不影响点、线目标的提取精度。在此基础之上,研究了稀疏SAR图像海上舰船目标检测方法,得益于较弱的背景噪声,稀疏SAR图像的目标检测使用简单的阈值处理即可获得较好的检测效果。Abstract: Sparse microwave imaging using sparse priors of observed scenes in space, time, frequency, or polarization domain and echo data with sampling rate smaller than the traditional Nyquist rate as well as optimization algorithms for reconstructing the microwave images of observed scenes has many advantages over traditional microwave imaging systems. In sparse microwave imaging, image acquisition and representation vary; therefore, new feature analysis and cognitive interpretation theories and methods should be developed based on current research results. In this study, we analyze the statistical properties of sparse Synthetic Aperture Radar (SAR) images and changes in point, line and regional features induced by sparse reconstruction. For SAR images recovered by the spatial sparse model, the statistical distribution degrades, whereas points and lines can be accurately extracted by low sampling rates. Furthermore, the target detection method based on sparse SAR images is studied. Owing to a weak background noise, target detection is easier using sparse SAR images than traditional ones.
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