A Spaceborne Tomographic SAR Reconstruction Method Based on Building Structural Characteristics

The acquisition of spaceborne tomographic data usually requires a longer period of time due to the satellite’s long revisit period. To address this issue, it is possible to leverage the similarity of neighboring pixels in order to perform tomographic reconstruction of building targets in urban areas. As a result, the insufficient number of samples can be approximately substituted by pixels with similar scattering characteristics. However, the current utilization of building structures is often limited to horizontal characteristics such as contour lines (CL); in addition, extraction methods either rely on external data as prior information or are constrained by the need to fit operations, which limits the shape of the contour lines. This paper proposes a spaceborne tomographic reconstruction method based on building characteristics, starting from data and fully utilizing the horizontal and vertical characteristics of buildings for reconstruction. First, interferometric information is used to assist in tomographic processing and a strategy combining multi-point growth with multi-level fusion is employed to extract contour lines. Additionally, the vertical characteristics of buildings are established to provide constraints on the solution space for tomographic processing. The three-dimensional reconstruction of isolated and vertical buildings is then achieved by combining signal elimination techniques. By more fully exploiting the structural characteristics of buildings, the proposed method is capable of recovering building structures even with a limited number of samples. The effectiveness of the proposed method is validated through simulated data and TerraSAR-X data.

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Publication Details

Journal
Remote Sensing
Published
2026-05-01
DOI
https://doi.org/10.3390/rs18091398
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
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article

A Spaceborne Tomographic SAR Reconstruction Method Based on Building Structural Characteristics

Jili Wang, Zi Wang, Y N Wu, Sisi Dong et al.
Remote Sensing
Synthetic Aperture Radar (SAR) Applications and Techniques
article

A Spaceborne Tomographic SAR Reconstruction Method Based on Building Structural Characteristics

Jili Wang, Zi Wang, Y N Wu, Sisi Dong, Weidong Yu
article en

Abstract

The acquisition of spaceborne tomographic data usually requires a longer period of time due to the satellite’s long revisit period. To address this issue, it is possible to leverage the similarity of neighboring pixels in order to perform tomographic reconstruction of building targets in urban areas. As a result, the insufficient number of samples can be approximately substituted by pixels with similar scattering characteristics. However, the current utilization of building structures is often limited to horizontal characteristics such as contour lines (CL); in addition, extraction methods either rely on external data as prior information or are constrained by the need to fit operations, which limits the shape of the contour lines. This paper proposes a spaceborne tomographic reconstruction method based on building characteristics, starting from data and fully utilizing the horizontal and vertical characteristics of buildings for reconstruction. First, interferometric information is used to assist in tomographic processing and a strategy combining multi-point growth with multi-level fusion is employed to extract contour lines. Additionally, the vertical characteristics of buildings are established to provide constraints on the solution space for tomographic processing. The three-dimensional reconstruction of isolated and vertical buildings is then achieved by combining signal elimination techniques. By more fully exploiting the structural characteristics of buildings, the proposed method is capable of recovering building structures even with a limited number of samples. The effectiveness of the proposed method is validated through simulated data and TerraSAR-X data.

Remote SensingVol. 18(9)
Chinese Academy of Sciences (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
Sustainable cities and communities
Openalex Percentile: Top 3%
Synthetic Aperture Radar (SAR) Applications and Techniques
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