Radargrammetric registration of airborne multi-aspect SAR data of urban areas

Schmitt, Michael; Maksymiuk, Oliver; Magnard, Christophe; Stilla, Uwe

doi:10.1016/j.isprsjprs.2013.09.003

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…In the experiment, we constructed the error equations using the range-Doppler (r-D) model. r-D model is a rigorous geometric model widely used in geometric processing of SAR images (Schmitt et al, 2013). There were 5 ground control points (GCPs) on the reference image and 6 GCPs on the sensed image.…”

Section: Accuracy Analysismentioning

confidence: 99%

Tie Points Extraction for Sar Images Based on Differential Constraints

Xiong¹,

Gao²,

Xu³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Automatically extracting tie points (TPs) on large-size synthetic aperture radar (SAR) images is still challenging because the efficiency and correct ratio of the image matching need to be improved. This paper proposes an automatic TPs extraction method based on differential constraints for large-size SAR images obtained from approximately parallel tracks, between which the relative geometric distortions are small in azimuth direction and large in range direction. Image pyramids are built firstly, and then corresponding layers of pyramids are matched from the top to the bottom. In the process, the similarity is measured by the normalized cross correlation (NCC) algorithm, which is calculated from a rectangular window with the long side parallel to the azimuth direction. False matches are removed by the differential constrained random sample consensus (DC-RANSAC) algorithm, which appends strong constraints in azimuth direction and weak constraints in range direction. Matching points in the lower pyramid images are predicted with the local bilinear transformation model in range direction. Experiments performed on ENVISAT ASAR and Chinese airborne SAR images validated the efficiency, correct ratio and accuracy of the proposed method.

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Section: Accuracy Analysismentioning

confidence: 99%

Tie Points Extraction for Sar Images Based on Differential Constraints

Xiong¹,

Gao²,

Xu³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Therefore, the same approach is used for fusing point clouds of multiple viewing directions in the context of this work. If several independent point clouds of the forested scene under investigation can be generated by the procedure described in the last sections, they may need to either be registered via their corresponding flight navigation data a priori (Schmitt, Maksymiuk, Magnard, & Stilla, 2013) or using point cloud-based registration methods a posteriori (e.g. Makadia, A. P., & Daniilidis, 2006;or Gernhardt, Cong, Eineder, Hinz, & Bamler, 2012).…”

Section: Fusion Of Point Clouds From Multiple Aspectsmentioning

confidence: 99%

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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“…After geocoding, these aspects should either be registered via their corresponding flight navigation data a priori [11] or using point cloud-based registration methods a posteriori [12]. If registration of the multi-aspect point clouds is ensured, all of them are aligned in the same common reference system.…”

Section: B Voxel-space-based Point Cloud Fusionmentioning

confidence: 99%

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

Schmitt

2015

2015 Joint Urban Remote Sensing Event (JURSE)

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Recent advances in airborne synthetic aperture radar tomography (TomoSAR) enable the generation of threedimensional point clouds of urban areas with sub-meter accuracy and a point density comparable to LiDAR by exploiting multi-baseline stacks from multiple viewing angles. This paper summarizes the complete workflow from tomographic height reconstruction via geocoding to the combination of multi-aspect point clouds by a novel voxel-space-based fusion strategy. The evaluation results with respect to LiDAR-derived reference data show the great potential of multi-aspect TomoSAR data fusion when it comes to accurate and comprehensive reconstruction of urban areas.

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Radargrammetric registration of airborne multi-aspect SAR data of urban areas

Cited by 12 publications

References 19 publications

Tie Points Extraction for Sar Images Based on Differential Constraints

Tie Points Extraction for Sar Images Based on Differential Constraints

Reconstruction of individual trees from multi-aspect TomoSAR data

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

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