Robust vegetation height Extraction using maximum likelihood estimation for Dual-baseline PolInSAR

Polarimetric SAR interferometry technique has been widely used for parameters extraction of the earth´s surface vegetation. In this paper, based on the two layers Random Volume over Ground model, we present a vegetation height inversion algorithm for dual-baseline PolInSAR data. The method obtained the ground and volume scattering component respectively by using the theory of Freeman polarimetric decomposition. Then the maximum likelihood estimation of the covariance matrix was used to construct the vegetation height for dual-baseline PolInSAR. The proposed algorithm overcomes the restriction of traditional maximum likelihood estimation method which required the parameters of ground scattering to be known. Finally, the experimental results of L-band PolInSAR simulated data show that the algorithm improves the effect of height estimation compare to the coherence method.