Abstract
Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA).
Highlights
Forests are the largest terrestrial ecosystem on Earth, and they play an irreplaceable role in the carbon cycle and sustainable development [1]
The ground scattering phase centers detected by the wavelet and orthogonal (W&O)-sparse iterative covariance-based estimation (SPICE) algorithm show a good consistency with the LiDAR digital terrain model (DTM). (3) For the HV polarization, the dominant scattering is the canopy backscattering, and the corresponding detected phase centers have a similar wave trend to the LiDAR canopy height model (CHM). (4) For the VV polarization, the W&O-SPICE algorithm detects some ground scattering phase centers and some canopy scattering phase centers
To further verify the performance of the SPICE algorithm based on the W&O sparse basis, the tomographic estimators of compressive sensing (CS) and the nonparametric spectral estimation methods (beamforming, Capon, and the iterative adaptive approach (IAA)) were applied in tomographic focusing of the same azimuth profile
Summary
Forests are the largest terrestrial ecosystem on Earth, and they play an irreplaceable role in the carbon cycle and sustainable development [1]. The vertical structure of a forest directly reflects the growth and development of the forest, but is an important and necessary input for estimating above-ground biomass and storage [2,3,4]. Since interferometric synthetic aperture radar (InSAR) can provide penetrability into forest, especially in the long wavelengths such as the L-band and P-band, it has become an invaluable tool for vertical structure reconstruction over forest areas. It cannot discriminate the different scatterers within one resolution cell, and the estimated height is the mean of all the scatterers’ heights. The idea behind the concept of TomoSAR is that it combines multiple acquisitions to form an additional synthetic aperture along the vertical direction, in addition to the conventional aperture along the azimuth direction [7,8]
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