Aircraft reconstruction in high-resolution SAR images using deep shape prior

Abstract

Contour shapes are important features. An accurate contour shape of a target can provide important prior information for applications, such as target recognition, which can improve the accuracy of target interpretation. In this paper, a synthetic aperture radar (SAR) target reconstruction method is proposed, which can be used to reconstruct the target by using shape priors to perform an accurate extraction of the contour shape feature. The method is divided into two stages. In the deep shape prior extraction stage, a generative deep learning modelling method is used to obtain deep shape priors. In the reconstruction stage, a novel coarse-to-fine pose estimation method combined with an optimization algorithm is proposed, which integrates deep shape priors into the process of reconstruction. Specifically, to address the issue of object rotation, candidate poses are obtained using the coarse pose estimation method, which avoids an exhaustive search of each pose. In addition, an energy function composed of a scattering term and shape term to combine the fine pose estimation, is constructed and optimized via an iterative optimization algorithm to achieve the goal of object reconstruction. To the best of our knowledge, this is the first time shape priors have been used to extract shape features of SAR targets. Experiments are conducted on a data set acquired by TerraSAR-X images and the results demonstrate the high accuracy and robustness of the proposed method.