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

Interferometric synthetic aperture radar (InSAR) techniques are powerful tool for reconstructing the 3-D position of scatterers, especially for the urban areas. Since the estimation accuracy depends on the inverse of number of interferograms and signal-to-noise ratio (SNR), it is necessary to use as many as possible interferograms in order to achieve more accurate result. However, the number of interferograms of TanDEM-X data is generally limited for most areas. Therefore, in order to maintain the estimation accuracy, one feasible way is to increase the SNR. In this work, we proposed a novel framework, which integrates the non-local procedure into SAR tomography inversion and combines the robust estimation. A large-scale demonstration has been carried out with five TanDEM-X bistatic data, which covers the entire city of Munich, Germany. Quantitative evaluation of the reconstructed result with the LiDAR reference exhibits the standard deviation of the height difference is within two meters, which implies the proposed framework has great potential for high quality large-scale 3-D urban modeling.

show abstract

“…There are still 34,054 buildings left after truncation. The standard deviation of height difference is about 1.96 m [24].…”

Section: B Quantitative Validationmentioning

confidence: 93%

Generation of Large-Scale High Quality 3-D Urban Models

Shi

Bamler

Wang

et al. 2020

2020 IEEE Radar Conference (RadarConf20)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The single source optimization of (10) can then be simplified aŝ k). (13) By replacing b(z, k) with b(z) in (13), one gets the SP expression derived in [49], which may be assimilated to a BF result, normalized by b(z) 2 , and evaluated at very low computational cost. In the polarimetric case, one cannot, as in the classical polarimetric mono-dimensional case, i.e., using P-CAPON and P-MUSIC, directly optimize (13) using an eigendecomposition, since b(z, k) 2 cannot be determined a priori, k being unknown.…”

Section: A Polarimetric Optimization Using Alternating Projectionsmentioning

confidence: 99%

“…Some neighborhood adaptive multidimensional SAR filtering methods [11], [12] have been designed to preserve point-like scatterers and fine textures, and reduce the speckle effect over complex environments. The impact of nonlocal adaptive filtering on 3-D reconstruction from TomoSAR data has been studied in [7] and [13]. The reconstruction of urban areas in 3-D using spaceborne SAR sensors recently experienced an important development, with the advent of HR SAR missions, such as TerraSAR-X (TSX) [3].…”

mentioning

confidence: 99%

3-D Characterization of Urban Areas Using High-Resolution Polarimetric SAR Tomographic Techniques and a Minimal Number of Acquisitions

Huang

Ferro-Famil

2021

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

This article addresses the 3-D reconstruction of urban areas using a minimal number of Synthetic Aperture Radar (SAR) acquisitions, that is, a set of three images, characterized by intermediate spatial resolution features. In such extreme conditions, conventional tomographic techniques reveal unadapted to refined 3-D imaging purposes, either due to the resulting intrinsic coarse vertical resolution, or to the low dimensionality of the data set, that prevents any separation of complex mixed scattering patterns. A new high-resolution (HR) tomographic estimator, based on a polarimetric signal subspace fitting criterion, is proposed to overcome these limitations, as this method adapts to the statistical behavior of the backscattered signals using robust metrics. The optimization of the corresponding focusing criterion is led through a new polarimetric alternating projection algorithm, characterized by a low computational cost, which may also be used to optimize the polarimetric the deterministic maximum likelihood criterion. The proposed polarimetric signal subspace fitting technique is shown to outperform the other studied HR techniques over both simulated signals and data acquired by the DLR's ESAR sensor at L-band over Dresden city, Germany. Finally full-rank polarimetric tomographic estimators are proposed that generalize nonparametric polarimetric estimators, and permit to estimate second-order polarimetric representations in 3-D, instead of unitary rank target vector with their conventional versions. This approach makes it possible to characterize polarimetric scattering mechanisms in 3-D.

show abstract

“…Shi et al [ 29 ] propose to increase SNR by integrating nonlocal estimation into the inversion and show a reasonable resconstruction of buildings from only seven interferograms. In [ 30 , 31 ], the authors show that the non-local TomoSAR framework can achieve a relative height accuracy of 2 m in large scale with a very small number of tracks (3–5 tracks). This method focuses on the reconstruction of a larger area and assumes that only a few dominant scatterers exist along the reflectivity profile.…”

Section: Introductionmentioning

confidence: 99%

Single Target SAR 3D Reconstruction Based on Deep Learning

Wang

Guo

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.

show abstract

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

Cited by 20 publications

References 39 publications

Generation of Large-Scale High Quality 3-D Urban Models

Generation of Large-Scale High Quality 3-D Urban Models

3-D Characterization of Urban Areas Using High-Resolution Polarimetric SAR Tomographic Techniques and a Minimal Number of Acquisitions

Single Target SAR 3D Reconstruction Based on Deep Learning

Contact Info

Product

Resources

About