Polarimetric object‐level SAR imaging method with canonical scattering characterisation by exploiting joint sparsity

Abstract

To identify potential man-made objects, traditional synthetic aperture radar (SAR) imaging techniques are used to consider the dependence in either frequency or azimuth domain. However, these existing methods may result in inaccurate scattering estimation without utilising the inherent polarimetric diversity property. To improve the imaging performance, a polarimetric object-level SAR imaging algorithm is proposed. In the scheme, the polarimetric SAR imaging with canonical scattering characterisation is transferred into a simultaneous sparse approximation (SSA) problem by virtual of incorporating sparse representation and canonical shape feature model. Then the SSA problem is solved via an efficient l 2 , p -norm ( p ∈ ( 0 , 1 ) ) regularisation algorithm. The main advantages of the proposed method are twofold: (i) considering the dispersive, anisotropic and polarimetric scattering characteristics of scatterers allows for more accurate estimation of physically relevant scattering geometry information of scattering centres in comparison with the method ignoring polarisation dependence; and (ii) by exploiting joint sparsity of the multiple polarisation measurements, it can effectively enhance the recovery accuracy in the consistency of the canonical scatterers’ number and locations in different channels. Experimental results are provided to verify the effectiveness of the proposed method.