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Multi-Angle SAR Sparse Image Reconstruction With Improved Attributed Scattering Model
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DOI：10.1109/LGRS.2019.2942476.png)
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Abstract
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The traditional synthetic aperture radar (SAR) sparse imaging methods are based on the point scattering model. However, this model is not suitable for many distributed targets with large variations in scattering characteristics at different angles, i.e., many distributed targets can no longer be considered as a combination of a series of ideal point scatterers under multi-angle observations. To solve this problem, by introducing the improved attributed scattering model into the traditional SAR echo model, we propose our multi-angle sparse image reconstruction method (MASIRM). Through modeling the illuminated scene with point scatterers and line-segment-scatterers, a multi-angle echo model is first presented. By generating an adaptive mixed dictionary and applying the pattern-coupled sparse Bayesian learning, the MASIRM obtains more geometric information of the distributed target with higher quality sparse SAR images. Real data experiments demonstrate that MASIRM performs favorably against traditional imaging methods.
Keywords:
Synthetic aperture radar
Adaptation models
Scattering
Radar polarimetry
Imaging
Radar imaging
Improved attributed scattering model
multi-angle synthetic aperture radar
sparse Bayesian learning
sparse image reconstruction
synthetic aperture radar (SAR)
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