Object‐level SAR imaging method with canonical scattering characterisation and inter‐subdictionary interferences mitigation

Abstract

Under the assumption of ideal-point scattering model, conventional developed synthetic aperture radar (SAR) imaging techniques often ignore frequency/angle dependence, which may result in scattering estimation inaccurate and object-level information loss. To solve the problem, in this study, the authors propose an object-level SAR imaging method by virtual of sparse representations and canonical shape feature models. Specifically, the representation basis vectors are combined with parametric scattering models for characterising angle/frequency dependence, so that they can accurately capture physically relevant scattering geometry information. To acquire high-quality object-level SAR image and mitigate the inter-subdictionary interferences, the authors propose a regularisation algorithm to take advantage of the inherent prior information including the sparsity of coefficient vector and the interaction nature between different spatial locations. The advantages of the proposed method can be summarised as follows: (i) by using the location-wise coordinate descent strategy, it can save memory cost and also reduce computation complexity in comparison with the direct solving the full inverse problem; and (ii) it can effectively acquire more parsimonious representation in the number of spatial locations and canonical scattering type information of strong scattering points such as top-hats and trihedral. Finally, experimental results are provided to demonstrate the validity of the proposed method.