Improvement of Attributed Scattering Center Extraction by Using SAR Super-Resolution Preprocessing

Abstract

Synthetic aperture radar (SAR) is able to acquire high-resolution method using the active microwave imaging method. SAR images are widely used in target recognition, classification, and surface analysis, with extracted features. Attribute scattering center (ASC) is able to describe the image features for these tasks. However, sidelobe effects reduce the accuracy and reliability of the estimated ASC model parameters. This paper incorporates the SAR super-resolution into the ASC extraction to improve its performance. Both filter bank and subspace methods are demonstrated for preprocessing to supress the sidelobe. Based on the preprocessed data, a reinforcement based ASC method is used to get the parameters. The experimental results show that the super-resolution method can reduce noise and suppress sidelobe effect, which improve accuracy of the estimated ASC model parameters.

Keywords

synthetic aperture radar (SAR) / spectrum estimation / attributed scattering center (ASC) / reinforcement learning