InSAR and Tomography for Large-Scale Urban Areas

As city is the central place people living and information spreading, three dimensional urban reconstruction not only has great significance, but it is also one urgent problem that people want to resolve. The three dimensional urban models play an important role in traffic, terra, mine, survey, and other fields, especially in city planning, construction, and environmentology. Modern spaceborne SAR sensors such as TerraSAR-X X, Tandem-X and COSMO-SkyMed can deliver meter-resolution data that fits well to the inherent spatial scales of buildings. This very high resolution (VHR) data is therefore particularly suited for detailed urban mapping. Therefore, to develop an InSAR data based reconstruction algorithms is highly relevant.

A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR

L1 regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super- resolution, and others and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued L1 regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with simulation data and real data, to demonstrate that the proposed approach can retain the accuracy of second order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as our example, the proposed method can be generally applied to our spectral estimation problems.

Non-Local Compressive Sensing Based SAR Tomography

Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This work proposes solutions for two notorious problems in this field: 1) TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. 2) CS-based inversion is computationally expensive and therefore barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the non-local L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high resolution spotlight images over an area in Munich, Germany.

SAR Tomography at the Limit: Building Height Reconstruction Using Only 3 – 5 TanDEM-X Bistatic Interferograms

Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas. In order to obtain a plausible reconstruction, it is necessary to use more than twenty interferograms. Hence, these methods are commonly not appropriate for large-scale 3-D urban mapping using TanDEM-X data where only a few acquisitions are available in average for each city. This work proposes a new SAR tomographic processing framework to work with those extremely small stacks, which integrates the non-local filtering into SAR tomography inversion. The applicability of the algorithm is demonstrated using a TanDEM-X multi-baseline stack with 5 bistatic interferograms over the whole city of Munich, Germany. Systematic comparison of our result with TanDEM-X raw digital elevation models (DEM) and airborne LiDAR data shows that the relative height accuracy of two third buildings is within two meters, which outperforms the TanDEM-X raw DEM. The promising performance of the proposed algorithm paved the first step towards high quality large-scale 3-D urban mapping.

Publikationen

  • Shi, Yilei; Zhu, Xiao Xiang; Bamler, Richard: Non-Local Compressive Sensing Based SAR Tomography. IEEE Transactions on Geoscience and Remote Sensing, 2018 mehr…
  • Zhu, Xiao Xiang; Baier, Gerald; Lachaise, Marie; Shi, Yilei; Adam, Fathalrahman; Bamler, Richard: Potential and Limits of Non-local Means InSAR Filtering for TanDEM-X High-resolution DEM Generation. Remote Sensing of Environment, 2018 mehr…
  • Ge, Nan; Gonzalez, Fernando Rodriguez; Wang, Yuanyuan; Shi, Yilei; Zhu, Xiao Xiang: Spaceborne Staring Spotlight SAR Tomography-A First Demonstration with TerraSAR-X. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018 mehr…
  • Shi, Yilei; Zhu, Xiao Xiang; Yin, Wotao; Bamler, Richard: A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR. IEEE Transactions on Geoscience and Remote Sensing, 2018 mehr…
  • Shi, Yilei; Zhu, Xiao Xiang; Bamler, Richard: SAR Tomography using Non-Local Sparse Reconstruction. IGARSS 2018, 2018 mehr…
  • Shi, Yilei; Wang, Yuanyuan; Bamler, Richard; Zhu, Xiao Xiang: Towards high-resolution global urban 3D model from TanDEM-X data. 5th Joint Workshop Urban Remote Sensing {--} Challenges & Solutions, 2018 mehr…
  • Zhu, Xiao Xiang; Sun, Yao; Ge, Nan; Shi, Yilei; Wang, Yuanyuan: Towards Global 3D/4D Urban Modeling Using TanDEM-X Data. EUSAR 2018, 2018 mehr…
  • Shi, Yilei; Wang, Yuanyuan; Kang, Jian; Lachaise, Marie; Zhu, Xiao Xiang; Bamler, Richard: 3D reconstruction from very small TanDEM-X stacks. EUSAR 2018, 2018 mehr…
  • Shi, Yilei; Zhu, Xiao Xiang; Bamler, Richard; Yin, Wotao: An efficient algorithm for compressive sensing based SAR tomography. EUSAR 2018, VDE Verlag, 2018 mehr…