Three-dimensional reconstruction of urban areas by multi-aspect TomoSAR data fusion

Schmitt, Michael

doi:10.1109/jurse.2015.7120478

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…This method is computationally more efficient than the one introduced in [42] due to the reduced number of points in the matching step. Relevant work in the airborne research domain can be found in [56] and [57]. It is important to note that all mentioned existing methods perform the point cloud fusion geometrically.…”

Section: Geodetic Fusion Of Tomosar Point Cloudsmentioning

confidence: 99%

Geodetic SAR Tomography

Zhu

Montazeri

Gisinger

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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In this paper, we propose a framework referred to as "geodetic synthetic aperture radar (SAR) tomography" that fuses the SAR imaging geodesy and tomographic SAR inversion (TomoSAR) approaches to obtain absolute 3-D positions of a large amount of natural scatterers. The methodology is applied on four very high resolution TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since all the TomoSAR estimates are relative to the same reference point object whose absolute 3-D positions are retrieved by means of stereo SAR, the point clouds reconstructed using data acquired from different viewing angles can be geodetically fused. To assess the accuracy of the position estimates, the resulting absolute shadowfree 3-D TomoSAR point clouds are compared with a digital surface model obtained by airborne LiDAR. It is demonstrated that an absolute positioning accuracy of around 20 cm and a meter-order relative positioning accuracy can be achieved by the proposed framework using TerraSAR-X data.

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Section: Geodetic Fusion Of Tomosar Point Cloudsmentioning

confidence: 99%

Geodetic SAR Tomography

Zhu

Montazeri

Gisinger

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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“…The core parameter of this procedure is the edge length d of the voxel cubes, which basically defines both the resolution of the fused point cloud as well as the degree of data and noise reduction. As has been shown in Schmitt (2015), while the combination of multi-aspect data already helps to fill previously shadowed scene parts, this fusion strategy additionally provides a measurable improvement of the 3D accuracy of the reconstructed 3D points, as well as a significant reduction of the number of points.…”

Section: Fusion Of Point Clouds From Multiple Aspectsmentioning

confidence: 80%

“…For the analysis of urban areas, multi-aspect data fusion has been demonstrated as a viable solution for this problem (e.g. Schmitt, 2015). Therefore, the same approach is used for fusing point clouds of multiple viewing directions in the context of this work.…”

Section: Fusion Of Point Clouds From Multiple Aspectsmentioning

confidence: 99%

“…If the data then are aligned in the same common reference system this way, an additional fusion step was proved to be beneficial: By combining the individual point clouds intelligently, not only the accuracy of the 3D data is improved, but also the number of points in the point cloud can be significantly reduced without any information loss. This greatly benefits any further processing due to reduction of computational costs and memory requirements (Schmitt, 2015). This fusion step can be implemented by a voxel-space-based strategy: In order to impose a voxel-space onto the registered, unstructured 3D point clouds, a regular point cloud containing the centers of the desired voxel space has to be created.…”

Section: Fusion Of Point Clouds From Multiple Aspectsmentioning

confidence: 99%

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Reconstruction of individual trees from multi-aspect TomoSAR data

Schmitt¹,

Shahzad²,

Zhu³

2015

Remote Sensing of Environment

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The localization and reconstruction of individual trees as well as the extraction of their geometrical parameters is an important field of research in both forestry and remote sensing. While the current state-of-the-art mostly focuses on the exploitation of optical imagery and airborne LiDAR data, modern SAR sensors have not yet met the interest of the research community in that regard. This paper presents a prototypical processing chain for the reconstruction of individual deciduous trees: First, single-pass multi-baseline InSAR data acquired from multiple aspect angles are used for the generation of a layover-and shadow-free 3D point cloud by tomographic SAR processing. The resulting point cloud is then segmented by unsupervised mean shift clustering, before ellipsoid models are fitted to the points of each cluster. From these 3D ellipsoids the relevant geometrical tree parameters are extracted. Evaluation with respect to a manually derived reference dataset prove that almost 74% of all trees are successfully segmented and reconstructed, thus providing a promising perspective for further research toward individual tree recognition from SAR data.

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Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

Domínguez

Small

Henke

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Digital surface model (DSMs) are sets of elevation data of the Earth's surface, useful for applications such as urban studies and height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric or tomographic configuration. Each image acquisition is usually focused in radar geometry. In this work, we present steps required to derive DSMs from SAR single-look complex (SLC) products focused in map geometry (geocoded). We modified existing tomographic reconstruction techniques to be able to operate with geocoded SLCs and extended methods to operate with 3D geocoded SLCs. The performance analysis showed that methods using 3D geocoded SLC products yielded DSMs with fewer outliers and retained more information of the illuminated area, with a cost of higher computational complexity. Compressive sensing methods using 2D geocoded SLCs can be a good alternative due to their comparatively moderate computational complexity. Index Terms-Synthetic aperture radar (SAR), tomography, urban, digital surface model (DSM).

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Three-dimensional reconstruction of urban areas by multi-aspect TomoSAR data fusion

Cited by 5 publications

References 15 publications

Geodetic SAR Tomography

Geodetic SAR Tomography

Reconstruction of individual trees from multi-aspect TomoSAR data

Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

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