Underlying topography and forest height estimation from SAR tomography based on a nonparametric spectrum estimation method with low sidelobes

Abstract

The underlying topography and forest height play an indispensable role in many fields, including geomorphology, civil engineering construction, forest investigation, and the modeling of natural disasters. As a new microwave remote sensing technology with three-dimensional imaging capability, synthetic aperture radar (SAR) tomography (TomoSAR) has already been proven to be an important tool for underlying topography and forest height estimation. Many spectrum estimation methods have now been proposed for TomoSAR. However, most of the commonly used methods are susceptible to noise and inevitably produce sidelobes, resulting in a reduced accuracy for the inversion of forest structural parameters. In this paper, to solve this problem, a nonparametric spectrum estimation method with low sidelobes–the G-Pisarenko method–is introduced. This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible. The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon, Africa. The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.