Resolution Enhanced SAR Tomography: From Match Filtering to Compressed Sensing Beamforming Techniques

摘要

For the analysis of forested scenes, the use of the sum of Kronecker products (SKP) decomposition techniquernallows the data covariance matrix of multipolarimetric multibaseline (MPMB) SAR surveys to be representedrnthrough a sum of two Kronecker products, composed of the polarimetric signatures and structural componentsrnfor ground and canopy, respectively. Thus, different tomographic SAR focusing methods can be applied on thernground and canopy structural components separately. Nevertheless, the SKP decomposition may result in rankdeficientrncovariance matrices, restricting the usage of adaptive beamforming techniques. For this reason, this paperrnconsiders two robust beamforming approaches, the wavelet-based compressed sensing (CS) technique andrnthe robust Capon beamforming (RCB) technique that incorporate additional constraints to guarantee robustnessrnagainst rank deficiencies of the data covariance matrix. The reported experimental results, obtained with a real PbandrnMPMB data set (courtesy of the German Aerospace Center), reveal that the RCB method significantly outperformsrnthe CS technique in the reduction of the computational complexity.