Volume XL-7/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W1, 193-198, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W1-193-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W1, 193-198, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W1-193-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Aug 2013

08 Aug 2013

SUPERRESOLUTION SAR IMAGING ALGORITHM BASED ON MVM AND WEIGHTED NORM EXTRAPOLATION

P. Zhang1, Q. Chen1, Z. Li1, Z. Tang2, J. Liu2, and L. Zhao2 P. Zhang et al.
  • 1Center for Earth Observation and Digital Earth Chinese Academy of Sciences No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
  • 2Beijing Institute of Spacecraft System Engineering,CAST, Beijing, 100094,China

Keywords: Synthetic Aperture Radar (SAR), Superresolution, Minimum Variance Method, Minimum Weighted Norm, Extrapolation

Abstract. In this paper, we present an extrapolation approach, which uses minimum weighted norm constraint and minimum variance spectrum estimation, for improving synthetic aperture radar (SAR) resolution. Minimum variance method is a robust high resolution method to estimate spectrum. Based on the theory of SAR imaging, the signal model of SAR imagery is analyzed to be feasible for using data extrapolation methods to improve the resolution of SAR image. The method is used to extrapolate the efficient bandwidth in phase history field and better results are obtained compared with adaptive weighted norm extrapolation (AWNE) method and traditional imaging method using simulated data and actual measured data.