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

Abstract

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.

Three-dimensional forest structure features extraction based on TomoSAR

“…Therefore, this paper chooses Capon algorithm to obtain forest 3D profile information. The power spectrum of reflectivity function estimated by Capon algorithm can be expressed as (Peng et al, 2021):…”

“…Among them, Synthetic Aperture Radar Tomography (TomoSAR) technology, as a 3D SAR imaging technique, effectively addresses the problem of layover in traditional SAR technology. It has been demonstrated to achieve the effective separation of internal scatterers within forest in the vertical direction, obtaining 3D structural profiles of forest (Pardini et al, 2021;Peng et al, 2021;Peng et al, 2018;Wang et al, 2022). In recent years, many scholars have been committed to extracting features related to forest structure from tomographic profiles, and then establishing a new way to express forest structure in a refined way by TomoSAR.…”

“…Therefore, using peak extraction functions can extract the height of canopy and ground scattering centers (Peng et al, 2018). After power compensation, basic forest structural indices such as underlying topography and forest height can be obtained (Luo et al, 2023;Peng et al, 2021;Wang et al, 2022;Zhang et al, 2023). Underlying topography is the basis of height information in forest structure, and forest height directly affects tree crown area and branch quality.…”

A Fourier–Legendre Polynomial Forest Height Inversion Model Based on a Single-Baseline Configuration

“…In fact, the vertical profile of the forest volume is complex, and it is difficult to express in terms of a definite function. In view of this, tomographic SAR (TomoSAR) has been used to estimate the vertical profile without depending on a scattering model [11,[21][22][23][24]. However, TomoSAR requires a large number of SAR images characterized by uniformly distributed baselines.…”