Abstract
Among 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.
Highlights
The upcoming space missions Tandem-L and BIOMASS, plan to create a 3-D map of the global forest structure by means of synthetic aperture radar (SAR) tomography (TomoSAR), and monitor the global carbon cycle in a systematic manner
To demonstrate the enhanced imaging capabilities of the multi-stage TomoSAR WAVAB technique (19), (20), we use a stack of nine single-look complex SAR images properly co-registered and phase flattened (Figure 6 shows one image out of the stack), obtained by processing fully polarimetric P-band data of the German Aerospace Center (DLR), acquired by the E-SAR airborne sensor, during the BioSAR campaign in October 2008, in the forested test area located at the Vindeln municipality, in northern Sweden
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Summary
The upcoming space missions Tandem-L and BIOMASS, plan to create a 3-D map of the global forest structure by means of synthetic aperture radar (SAR) tomography (TomoSAR), and monitor the global carbon cycle in a systematic manner. Forested scenes are characterized by a continuous distribution of scatterers along the vertical axis, and are composed of few prominent point-type scatterers For such scenarios, SAR sensors operating in longer wavelengths, e.g., L- and P-band, are commonly chosen, having better sensitivity to the contributions from both the ground surface and the vegetation layers [5,6]. Overcoming the above-mentioned disadvantages of the conventional MSF and Capon beamforming methods, in the real-world TomoSAR operating scenarios, and with significantly lower computational burden in comparison to the wavelet-based CS techniques, a new nonparametric multi-stage approach for feature enhanced TomoSAR imaging is addressed in this paper. The feature enhancing capabilities of the WAVAB TomoSAR imaging technique are corroborated via processing E-SAR airborne real data of the German Aerospace Center, providing detailed volume height profiles reconstruction as an alternative to the existing most prominent competing non-parametric SSA-based approaches
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