Comparison of Model Based and Image Based Surface Parameters Estimation from Polarimetric SAR

摘要

Surface can be characterized in terms of its material (dielectric) and geometric properties. The dielectric properties of the surface are expressed primarily by its moisture content, while the roughness describes the geometric characteristics of surface. Various techniques for information retrieval from remotely sensed data have been proposed in a number of recent studies. Some of them are based on an empirical relationship between the measured return signals and the ground truth. Because of their development from a limited number of observations, these models are generally valid only for the conditions under which those measured data were taken. These models also appear that no dependence on the roughness parameter, l-correlation length. In this work, the potential of using the polarimetric SAR data over surface scatterers in order to invert surface parameters is investigated. The model-based and image-based inversion schemes are investigated and compared; the former is doing retrieval from a dynamic learning neural network trained with the Advanced Integral Equation Model, while the latter is schemed from a decomposition of coherency matrix. In model based approach, only the surface scattering term of total return is used in order to remove the vegetation effects. The image based approach accounts for non-zero cross-polarized, backscattering as well as depolarization by three polarimetric parameters, namely the scattering entropy(H), the scattering anisotropy(A), and the alpha angle(alpha). The features of these two schemes are discussed in terms of numerical aspects and physical implications of the surface parameters being inverted by using experimental E-SAR L-band data . We also show the performances of inversion and discuss the advantages and drawbacks of both schemes.