The side-looking imaging geometry of synthetic aperture
radar (SAR) causes inevitable layover in SAR images.
Separating the contributions from different scatterers has
been the fundamental for many applications. It is typically
solved by explicit inversion of the SAR imaging model to
retrieve the scattering profile along the mixed dimension
(elevation), which is otherwise known as SAR tomography.
This paper proposed a robust blind scatterer separation
method to demix the layovered scatterers, avoiding the
computationally expensive tomographic inversion. We
demonstrate that the state-of-the-art principle component
decomposition-based methods are heavily influenced by the
nonergodicity of the selected samples, especially in urban
area, such as point scatterers appearing often on facades.
The proposed method is shown to be more robust than the
state-of-the-art. Real data example shows that the proposed method outperforms the state-of-the-art by a factor of three in terms of the accuracy of the retrieved phase.
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The side-looking imaging geometry of synthetic aperture
radar (SAR) causes inevitable layover in SAR images.
Separating the contributions from different scatterers has
been the fundamental for many applications. It is typically
solved by explicit inversion of the SAR imaging model to
retrieve the scattering profile along the mixed dimension
(elevation), which is otherwise known as SAR tomography.
This paper proposed a robust blind scatterer separation
method to demix the layovered scatterers, av...
»