Structure-Aware Interrupted SAR Imaging Method for Change Detection

By exploiting the continuity structure of target scene, the problem of interrupted synthetic aperture radar (SAR) imaging for change detection is studied in this paper. Timeline constraints imposed on multi-function modern radars lead to gapped SAR data collections, which in turn results in corrupted image that degrades reliable coherent change detection (CCD). In this paper we extrapolate the missing data using the sparse Bayesian framework. In particular, the inherent clustered structures of the sparse target scene are characterized by structure-aware Bayesian priors. The variational Bayesian inference (VBI) is then utilized to estimate an approximated posterior of the sparse coefficients. Finally the CCD images are obtained by applying the coherence estimator to the resultant complex images. Based on the structural information in the imaging process, the devised method offers the advantages of preserving the weak scatterers and suppressing the artificial points with fewer measurements. Experimental results are presented to demonstrate the effectiveness and superiority of the proposed algorithm.

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