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


Below the belief of ideal-purpose scattering model, typical developed artificial aperture radar (SAR) imaging techniques usually ignore frequency/angle dependence, that might result in scattering estimation inaccurate and object-level info loss. To resolve the matter, 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, thus that they'll accurately capture physically relevant scattering geometry information. To amass high-quality object-level SAR image and mitigate the inter-subdictionary interferences, the authors propose a regularisation algorithm to take advantage of the inherent previous data including the sparsity of coefficient vector and therefore the interaction nature between different spatial locations. The advantages of the proposed methodology can be summarised as follows: (i) by using the placement-wise coordinate descent strategy, it will save memory price and additionally scale back computation complexity as compared with the direct solving the full inverse drawback; and (ii) it can effectively acquire additional parsimonious illustration in the amount of spatial locations and canonical scattering type info of robust scattering points like prime-hats and trihedral. Finally, experimental results are provided to demonstrate the validity of the proposed method.

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