3D forest structure estimation from sar tomography by means of a full rank polarimetric inversion based on compressive sensing

Abstract

SAR tomography (TomoSAR) techniques allow a direct 3D imaging by exploiting angular diversity with different passes of the sensor. One of the main drawbacks of SAR tomography is that the estimation of the vertical reflectivity profile has to be performed through a limited set of multibaseline acquisitions, which requires solving a highly underdetermined system of equations. In TomoSAR literature, the Capon and the Fourier beamforming spectral estimators are widely employed. As an alternative, the application of Compressive Sensing (CS) techniques to the estimation of forest profiles has been recently introduced. In this paper, a different algorithm based on CS is proposed. It performs a full rank polarimetric inversion, allowing thus an estimation of the 3D coherency matrices. To study the full rank polarimetric TomoSAR inversion, a temporal series of airborne data is used. The results of the 3D polarimetric inversion will be contrasted to in situ measurements and LIDAR data.