TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach
AbstractAmong the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods. View Full-Text
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Martín del Campo, G.D.; Shkvarko, Y.V.; Reigber, A.; Nannini, M. TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach. Remote Sens. 2018, 10, 1822.
Martín del Campo GD, Shkvarko YV, Reigber A, Nannini M. TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach. Remote Sensing. 2018; 10(11):1822.Chicago/Turabian Style
Martín del Campo, Gustavo D.; Shkvarko, Yuriy V.; Reigber, Andreas; Nannini, Matteo. 2018. "TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach." Remote Sens. 10, no. 11: 1822.
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