Gongjian Wen

TL;DR: A statistics-based distance measure is designed to evaluate the distance between individual ASCs and the Hungarian algorithm is employed to build a one-to-one correspondence between two ASC sets, providing a reliable and robust similarity measure for SAR ATR.

TL;DR: A robust similarity measure for two attributed scattering center (ASC) sets and applies it to synthetic aperture radar (SAR) automatic target recognition (ATR) and Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset verify the validity and robustness of the proposed method.

TL;DR: The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition are given by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs).

TL;DR: This paper uses the binary target region as the feature and proposes a matching scheme for the target regions using binary morphological operations and employs a Bayesian decision fusion to fuse the similarities gained by different structuring elements to further enhance the recognition performance.

TL;DR: By the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system.