Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset

The underlying topography is an important part of the three-dimensional structure of forests, and is used for a variety of applications, such as hydrology and water resource management, civil engineering projects, and forest resource surveying. Due to the three-dimensional imaging ability and strong penetration, the tomographic synthetic aperture radar (TomoSAR) with a long wavelength has been shown to be a useful tool to estimate the underlying topography. At present, most of the current methods use the local means method to estimate the sample covariance matrix, in which the vertical backscattering power is estimated. However, these methods cannot easily obtain high-precision underlying topography, and often lose some detailed information. In this paper, to solve this problem, a non-local means method is introduced to estimate the optimal covariance matrix by combining weighted neighborhood pixels. To validate the feasibility and effectiveness of this proposed method, a BioSAR 2008 campaign L-band dataset acquired from the northern forests of Sweden was used to inverse the underlying topography. The results show that the accuracy of the underlying topography retrieved by the proposed method is improved by more than 30% when compared with the traditional method.