Geometric primitive extraction for 3D reconstruction of urban areas from tomographic SAR data

D'Hondt, O.; Guillaso, Stéphane; Hellwich, Olaf

doi:10.1109/jurse.2013.6550701

Cited by 16 publications

(10 citation statements)

References 9 publications

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“…This corresponds to the method presented in (D'Hondt et al, 2013a). In this case, several buildings are not well separated from the ground.…”

Section: Methodsmentioning

confidence: 94%

“…First attempts to automatically interpret such data are geometric primitive extraction from airborne data (D'Hondt et al, 2013a) and facade extraction from very high resolution spaceborne data (Shahzad and Zhu, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Our method relies on a planar primitive extraction similar to the one of (D'Hondt et al, 2013a) but improves the approach in two aspects. First, the segmentation of the tomographic height map is guided by the radiometric reflectivity information from the images.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Semantic Interpretation of Urban Areas With Airborne Synthetic Aperture Radar Tomography

D'Hondt¹,

Guillaso²,

Hellwich³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we introduce a method to detect and reconstruct building parts from tomographic Synthetic Aperture Radar (SAR) airborne data. Our approach extends recent works in two ways: first, the radiometric information is used to guide the extraction of geometric primitives. Second, building facades and roofs are extracted thanks to geometric classification rules. We demonstrate our method on a 3 image L-Band airborne dataset over the city of Dresden, Germany. Experiments show how our technique allows to use the complementarity between the radiometric image and the tomographic point cloud to extract buildings parts in challenging situations.

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“…This corresponds to the method presented in (D'Hondt et al, 2013a). In this case, several buildings are not well separated from the ground.…”

Section: Methodsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Towards a Semantic Interpretation of Urban Areas With Airborne Synthetic Aperture Radar Tomography

D'Hondt¹,

Guillaso²,

Hellwich³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It has been successfully used for the reconstruction of the vertical structure of forest [22,23], ice subsurface imaging [24], and the reconstruction of urban objects (e.g., in [25,26]). While most works focus either on the generation of TomoSAR data or on its geometric processing, e.g., regularization [27], geometric primitive extraction [28,29], or object modelling [30], only a few works address semantic analysis. Recently, TomoSAR descriptors have been proposed in [31] for the classification of forest development stages.…”

Section: Introductionmentioning

confidence: 99%

Exploiting SAR Tomography for Supervised Land-Cover Classification

D'Hondt

Hänsch

Wagener³

et al. 2018

Remote Sensing

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In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extracted from the tomograms. We also provide a comparison of classification results obtained from covariance matrices versus tomogram features as well as obtained by different reference methods, i.e., the traditional Wishart classifier and the more sophisticated Random Forest. Extensive qualitative and quantitative results are shown on a fully polarimetric and multi-baseline dataset from the E-SAR sensor from the German Aerospace Center (DLR).

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“…Object reconstruction from spaceborne TomoSAR point cloud has been recently started (D'Hondt et al, 2013) (Shahzad and Zhu, 2015a) (Fornaro et al, 2014). These point clouds have point density in the range of 600,000 ~ 1,000,000 points/km 2 and are associated with some characteristics that are worth to mention (Zhu and Shahzad, 2014): 1) TomoSAR point clouds deliver moderate 3D positioning accuracy on the order of 1 m; 2) Few number of images and limited orbit spread render the location error of TomoSAR points highly anisotropic, with an elevation error typically one or two orders of magnitude higher than in range and azimuth (Zhu and Bamler, 2012); 3) Due to the coherent imaging nature, temporally incoherent objects such as trees cannot be reconstructed from multipass spaceborne SAR image stacks; 4) TomoSAR point clouds possess much higher density of points on the building façades due to side looking SAR geometry enabling systematic reconstruction of buildings footprint via façade points analysis.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Building Footprints Using Spaceborne Tomosar Point Clouds

Shahzad¹,

Zhu

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: TerraSAR-X/TanDEM-X, tomographic SAR (TomoSAR) inversion, 3-D point cloud, building footprint, clustering ABSTRACT:This paper presents an approach that automatically (but parametrically) reconstructs 2-D/3-D building footprints using 3-D synthetic aperture radar (SAR) tomography (TomoSAR) point clouds. These point clouds are generated by processing SAR image stacks via SAR tomographic inversion. The proposed approach reconstructs the building outline by exploiting both the roof and façade points. Initial building footprints are derived by applying the alpha shapes method on pre-segmented point clusters of individual buildings. A recursive angular deviation based refinement is then carried out to obtain refined/smoothed 2-D polygonal boundaries. A robust fusion framework then fuses the information pertaining to building façades to the smoothed polygons. Afterwards, a rectilinear building identification procedure is adopted and constraints are added to yield geometrically correct and visually aesthetic building shapes. The proposed approach is illustrated and validated using TomoSAR point clouds generated from a stack of TerraSAR-X high-resolution spotlight images from ascending orbit covering approximately 1.5 km 2 area in the city of Berlin, Germany.

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Geometric primitive extraction for 3D reconstruction of urban areas from tomographic SAR data

Cited by 16 publications

References 9 publications

Towards a Semantic Interpretation of Urban Areas With Airborne Synthetic Aperture Radar Tomography

Towards a Semantic Interpretation of Urban Areas With Airborne Synthetic Aperture Radar Tomography

Exploiting SAR Tomography for Supervised Land-Cover Classification

Reconstruction of Building Footprints Using Spaceborne Tomosar Point Clouds

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