基于迁移学习的SAR目标超分辨重建

doi:10.7527/S1000-6893.2014.0348

Abstract

Abstract:

Based on transfer learning, a method for synthetic aperture radar(SAR) target super-resolution reconstruction is proposed in this paper. A semi-coupled dictionary is jointly trained in the gradient domain of optical image. By utilizing the relationship revealed by semi-coupled dictionary, the sparse codes of SAR image are obtained. Then the image is reconstructed in the high resolution dictionary. Based on some prior knowledge of SAR image, the regularization method is also used in order to enhance the target feature. Several simulation experiments are conducted based on TerraSAR-X and MSTAR data, and the reconstructed results show that the spatial resolution obtained by the proposed method is 0.5-1.5 pixels higher compared to the current interpolation method as well as the sparse representation method. Regularization enhancement results show that it can further improve the spatial resolution and suppress clutters by introducing the sparse prior. Finally, the influences on the spatial resolution and target structure of the reconstruction image caused by regularization parameter are analyzed qualitatively.

Key words: synthetic aperture radar, super-resolution, transfer learning, semi-coupled dictionary, sparse representation

CLC Number:

XU Zhou, QU Changwen, HE Lingqi. SAR target super-resolution based on transfer learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(6): 1940-1952.

References

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SAR target super-resolution based on transfer learning

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