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Introducing spatial regularization in SAR tomography reconstruction

Abstract : The resolution achieved by current Synthetic Aperture Radar (SAR) sensors provides detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building façade, roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: (i) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing); (ii) transformation from radar geometry to ground geometry and extraction of significant scat-terers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale non-convex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate on simulated data and a TerraSAR-X tomographic dataset the potential of this approach to produce 3-D reconstructions of urban surfaces.
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Submitted on : Tuesday, April 30, 2019 - 5:49:23 PM
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  • HAL Id : hal-01797425, version 2

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Clément Rambour, Loïc Denis, Florence Tupin, Hélène Oriot. Introducing spatial regularization in SAR tomography reconstruction. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2020. ⟨hal-01797425v2⟩

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