DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry

Feng, Shanshan; Lin, Yun; Wang, Yanping; Yang, Yanhui; Shen, Wenjie; Teng, Fei; Hong, Wen

doi:10.3390/rs12030556

Cited by 17 publications

(9 citation statements)

References 24 publications

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“…Multi-angular SAR can extract the DEM of the scene using its own data. This has already been a necessary and mature step of the imaging procedure for us [22,45], so the proposed method can be used for an unknown area. Except the height error caused by the DEM model, 3D model of the complex target will lead to layovers too.…”

Section: Discussionmentioning

confidence: 99%

An Anisotropic Scattering Analysis Method Based on the Statistical Properties of Multi-Angular SAR Images

et al. 2020

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The scatterings of many targets are aspect dependent, which is called anisotropy. Multi-angular synthetic aperture radar (SAR) is a suitable means of detecting this kind of anisotropic scattering behavior by viewing targets from different aspect angles. First, the statistical properties of anisotropic and isotropic scatterings are studied in this paper. X-band chamber circular SAR data are used. The result shows that isotropic scatterings have stable distributions in different aspect viewing angles while the distributions of anisotropic scatterings are various. Then the statistical properties of single polarization high-resolution multi-angular SAR images are modeled by different distributions. G 0 distribution performs best in all types of areas. An anisotropic scattering analysis method based on the multi-angular statistical properties is proposed. A likelihood ratio test based on G 0 distribution is used to measure the anisotropy. Anisotropic scatterings can be discriminated from isotropic scatterings by thresholding. Besides, the scattering direction can also be estimated by our method. AHH polarization C-band circular SAR data are used to validate our method. The result of using G 0 distribution is compared with the result of using Rayleigh distribution. The result of using G 0 distribution is the better one.

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Section: Discussionmentioning

confidence: 99%

An Anisotropic Scattering Analysis Method Based on the Statistical Properties of Multi-Angular SAR Images

et al. 2020

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“…For comparison, we also implemented a hierarchical normalized cross-correlation (NCC) matching algorithm for the epipolar pyramids to generate the DSMs for the Song Mountain study area. NCC is still the most common SAR imagery matching method, no matter for DSM reconstruction [22,35,36] or for surface motion estimation by pixel offset tracking technique [37][38][39]. The hierarchical NCC matching strategy is widely used in radargrammetry.…”

Section: Hierarchical Ncc Matchingmentioning

confidence: 99%

“…In terms of radargrammetric DSM generation, local matching has been the dominant matching approach since the 1980s [18][19][20][21][22]. No work has been published to investigate the feasibility and effectiveness of performing pixel-wise dense matching methods for more than two decades.…”

Section: Introductionmentioning

confidence: 99%

Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions

et al. 2022

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Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.

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“…Consider that a point T(x T , y T , z T ) is above the reference ground plane where the height of the imaging plane is z = z p . As is shown in Figure 1, if the elevation of the imaging plane is not consistent with the actual elevation of the target, the two dimensional coordinate of the target on the sub-aperture image will deviate from the actual position, resulting in geometric deformation of the full-aperture image [27]. Here we take viewing angle B for example to explain the imaging geometry of the target point, shown in Figure 2.…”

Section: Inversely Mapping and Votingmentioning

confidence: 99%

3D Point Cloud Reconstruction Using Inversely Mapping and Voting from Single Pass CSAR Images

et al. 2021

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3D reconstruction has raised much interest in the field of CSAR. However, three dimensional imaging results with single pass CSAR data reveals that the 3D resolution of the system is poor for anisotropic scatterers. According to the imaging mechanism of CSAR, different targets located on the same iso-range line in the zero doppler plane fall into the same cell while for the same target point, imaging point will fall into the different positions at different aspect angles. In this paper, we proposed a method for 3D point cloud reconstruction using projections on 2D sub-aperture images. The target and background in the sub-aperture images are separated and binarized. For a projection point of target, given a series of offsets, the projection point will be mapped inversely to the 3D mesh along the iso-range line. We can obtain candidate points of the target. The intersection of iso-range lines can be regarded as voting process. For a candidate, the more times of intersection, the higher the number of votes, and the candidate point will be reserved. This fully excavates the information contained in the angle dimension of CSAR. The proposed approach is verified by the Gotcha Volumetric SAR Data Set.

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DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry

Cited by 17 publications

References 24 publications

An Anisotropic Scattering Analysis Method Based on the Statistical Properties of Multi-Angular SAR Images

An Anisotropic Scattering Analysis Method Based on the Statistical Properties of Multi-Angular SAR Images

Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions

3D Point Cloud Reconstruction Using Inversely Mapping and Voting from Single Pass CSAR Images

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