Abstract
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.
Highlights
The emerging synthetic aperture radar (SAR) technique has been widely utilized in military and civilian applications in the past decades
Considerable efforts have been made to deal with missing data in SAR image formation processing
To tackle the above problems, in this paper, a general structure-aware interrupt SAR imaging method is developed under the Bayesian framework, with the goal of achieving reliable change detection
Summary
The emerging synthetic aperture radar (SAR) technique has been widely utilized in military and civilian applications in the past decades. Y. Gan et al.: Structure-Aware Interrupted SAR Imaging Method for Change Detection gains because of the introducing of common spatial reflectivity support constraints in the multi-pass data processing. To further improve the performance of radar imaging, the inherent structures of targets underlying sparsity patterns are considered by placing block or cluster structural information on the scatterers. The block-OMP method [14] and the group LASSO method [15] are able to obtain enhanced reconstruction performance in the block sparse signals case In these methods, the groups or blocks of the sparse coefficients are pre-specified, but the prior information about the block partition of coefficients is often practically unavailable. To tackle the above problems, in this paper, a general structure-aware interrupt SAR imaging method is developed under the Bayesian framework, with the goal of achieving reliable change detection.
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