Abstract Object reconstruction is of vital importance in Synthetic Aperture Radar (SAR) image analysis. In this paper, we propose a novel method based on shape prior to reconstruct aircraft in high resolution SAR images. The method mainly contains two stages. In the shape prior modeling stage, a generative deep learning method is used to model deep shape priors; a novel framework is then proposed in the reconstruction stage, which integrates the shape priors in the process of reconstruction. Specifically, to address the issue of object rotation, a novel pose estimation method is proposed to obtain candidate poses, which avoids making an exhaustive search for each pose. In addition, an energy function combining a scattering region term and a shape prior term is proposed; this is optimized via an iterative optimization algorithm to achieve the goal of object reconstruction. To the best of our knowledge, this is the first attempt made to reconstruct objects with complex shapes in SAR images using deep shape priors. Experiments are conducted on the dataset acquired by TerraSAR-X and results demonstrate the accuracy and robustness of the proposed method.
Fund: The National Natural Science Foundation of China (61331017)
Cite this article:
Dou Fangzheng,Diao Wenhui,Sun Xian et al. Aircraft Reconstruction in High Resolution SAR Images Using Deep Shape Prior[J]. JOURNAL OF RADARS, 2017, 6(5): 503-513.
Liu Zeyu, Liu Bin, Guo Weiwei, Zhang Zenghui, Zhang Bo, Zhou Yueheng, Ma Gao, Yu Wenxian. Ship Detection in GF-3 NSC Mode SAR Images[J]. JOURNAL OF RADARS, 2017, 6(5): 473-482.
2017, Vol. 6 
