Sparse Representation Based Algorithm for Estimation of Attributed Scattering Center Parameter on SAR imagery

Considering the sparsity of the frequency-aspect backscattered data in the attributed scattering center model parameter domain, a novel method based on sparse representation is proposed to extract attributed scattering center and estimate parameters in frequency-aspect domain. Due to the high dimension of model parameter, one high dimensional joint dictionary needs to be constructed, which may cost a mass storage. In this paper, two low dimensional dictionaries including localization and aspect attribute parameters respectively are constructed to replace the high dimensional joint dictionary to decouple the range characteristic and aspect characteristic, and reduce resource cost; Orthogonal Matching Pursuit (OMP) combined with RELAX are utilized to find the solution of the minimuml0 norm optimization issue and estimate localization parameters and aspect attribute parameters simultaneously. With the extracted attributed scattering centers, geometrical dimensions of the target or its main structure can be estimated. Numerical results both on electromagnetic computation data and measured data verify the validity of the proposed method.