Robust and Fast Super-Resolution SAR Tomography of Forests Based on Covariance Vector Sparse Bayesian Learning

A novel method based on covariance vector sparse Bayesian learning (CV-SBL) is proposed in this letter to reconstruct the vertical structure of forests using a small number of synthetic aperture radar (SAR) images. This method regards the inversion of forests reflectivity profiles in the wavelet domain as a sparse signal reconstruction (SSR). Based on the covariance matrix matching criterion, the backscatter power of forests signal and noise will be jointly solved adaptively. Through a few iterations, the exact positions of the closely spaced phase centers can be obtained to simplify the characterization of the vertical structure of the forests. Unlike the traditional compressive sensing (CS) method based on $\ell _{1} $ norm convex optimization, the novel method can obtain a real sparse solution without setting hyper-parameters and has a more reliable and accurate reconstruction performance. Besides, the computational efficiency of the new method is much higher than that of the $\ell _{1} $ minimization CS method, and it is more suitable for large-scale forests mapping applications. The proposed method is validated using P-band TropiSAR 2009 data set over a test site in Paracou, French Guiana. Furthermore, the reconstruction performance of the proposed method is compared with the spectral analysis methods (Beamforming and Capon) and the $\ell _{1} $ minimization super-resolution CS.