Baseline distribution optimization and missing data completion in wavelet-based CS-TomoSAR

Abstract

In this paper, we propose a coherence of measurement matrix-basedbaseline distribution optimization criterion, together with an ${L_1}$regularization missing data completion method for unobserved baselines(not belonging to the actual baseline distribution), to facilitatewavelet-based compressive sensing-tomographic synthetic aperture radarimaging (CS-TomoSAR) in forested areas. Using $M$ actual baselines, wefirst estimate the optimal baseline distribution with $N$ baselines$(N~>~M)$, including $N-M$ unobserved baselines, via the proposedcoherence criterion. We then use the geometric relationship between theactual and unobserved baseline distributions to reconstruct thetransformation matrix by solving an ${L_1}$ regularization problem, andcalculate the unobserved baseline data using the measurements of actualbaselines and the estimated transformation matrix. Finally, we exploitthe wavelet-based CS technique to reconstruct the elevation via thecompleted data of $N$ baselines. Compared to results obtained using onlythe data of actual baselines, the recovered image based on the datasetobtained by our proposed method shows higher elevation recovery accuracyand better super-resolution ability. Experimental results based onsimulated and real data validated the effectiveness of the proposed method.