SUPERRESOLUTION SAR IMAGING ALGORITHM BASED ON MVM AND WEIGHTED NORM EXTRAPOLATION
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2013-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W1/193/2013/isprsarchives-XL-7-W1-193-2013.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |