Towards Feature Enhanced SAR Tomography: A Maximum-Likelihood Inspired Approach
| Autor: | Gustavo Martin del Campo, Matteo Nannini, Andreas Reigber |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Synthetic aperture radar
010504 meteorology & atmospheric sciences Computer science Aperture synthetic aperture radar (SAR) tomography (TomoSAR) 0211 other engineering and technologies Maximum-likelihood (ML) 02 engineering and technology White noise Inverse problem Geotechnical Engineering and Engineering Geology 01 natural sciences Reduction (complexity) Noise Tomography Electrical and Electronic Engineering spectral analysis (SA) Decorrelation Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences Parametric statistics |
| DOI: | 10.1109/lgrs.2018.2858571 |
| Popis: | One of the main objectives of the upcoming space missions, such as Tandem-L and BIOMASS, is to map, on a global scale, the forest structure by means of synthetic aperture radar (SAR) tomography (TomoSAR). On one hand, the number of baselines is constrained to the revisit time that avoids temporal decorrelation issues. On the other hand, enhanced resolution is desired, since the forest structure is characterized from the vegetation layers that compose it, reflected in the tomographic profiles as local maxima. The TomoSAR nonlinear ill-conditioned inverse problem is conventionally tackled within the direction-of-arrival (DOA) estimation framework. The DOA-inspired nonparametric techniques are well suited to cope with distributed targets; nonetheless, the achievable resolution highly depends on the span of the tomographic aperture. Alternatively, superresolved parametric approaches have the main drawback related to the white noise model assumption that guaranties the separation of the signal and noise subspaces. Overcoming the disadvantages of the aforementioned techniques, in this letter, we address a novel maximum-likelihood (ML) inspired adaptive robust iterative approach (MARIA) for feature-enhanced TomoSAR reconstruction. MARIA performs resolution enhancement, with suppression of artifacts and ambiguity levels reduction, to an initial estimate of the continuous power spectrum pattern. After convergence, an accurate location of the closely spaced phase centers is achieved, easing the characterization of the forest structure. The feature-enhancing capabilities of the proposed approach are corroborated using airborne F-SAR data of the German Aerospace Center (DLR). |
| Databáze: | OpenAIRE |
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