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      Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images

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          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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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 February 2016
          : 5
          : 1
          : 42-56
          Affiliations
          [1 ] Shanghai Key Laboratory of Intelligent Sensing and Recognition, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University
          Article
          6cbbd8f2a61c4c02afd791e4d1fbc527
          10.12000/JR15097

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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