3d Surface Reconstruction From Multi-Date Satellite Images

Bullinger, Sebastian; Bodensteiner, Christoph; Arens, Michael

doi:10.5194/isprs-archives-xliii-b2-2021-313-2021

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…However, there has been limited research on reconstruction 3D mesh that offers advantages in texturing, visualization, rendering, and editing. Some methods [8,9] can produce a mesh model from the point cloud. However, those processes inevitably introduce cumulative errors.…”

Section: Introductionmentioning

confidence: 99%

Sat-Mesh: Learning Neural Implicit Surfaces for Multi-View Satellite Reconstruction

Qu,

Deng

2023

Remote Sensing

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Automatic reconstruction of surfaces from satellite imagery is a hot topic in computer vision and photogrammetry. State-of-the-art reconstruction methods typically produce 2.5D elevation data. In contrast, we propose a one-stage method directly generating a 3D mesh model from multi-view satellite imagery. We introduce a novel Sat-Mesh approach for satellite implicit surface reconstruction: We represent the scene as a continuous signed distance function (SDF) and leverage a volume rendering framework to learn the SDF values. To address the challenges posed by lighting variations and inconsistent appearances in satellite imagery, we incorporate a latent vector in the network architecture to encode image appearances. Furthermore, we introduce a multi-view stereo constraint to enhance surface quality. This constraint minimizes the similarity between image patches to optimize the position and orientation of the SDF surface. Experimental results demonstrate that our method achieves superior visual quality and quantitative accuracy in generating mesh models. Moreover, our approach can learn seasonal variations in satellite imagery, resulting in texture mesh models with different and consistent seasonal appearances.

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Section: Introductionmentioning

confidence: 99%

Sat-Mesh: Learning Neural Implicit Surfaces for Multi-View Satellite Reconstruction

Qu,

Deng

2023

Remote Sensing

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“…Urban buildings are also essential to the USS and are important carriers of human social and economic activities [30]. The studies of edge extraction of urban buildings and building height extraction based on high-resolution remote sensing images have also become research hotspots [31][32][33][34][35]. With accelerated urbanization, the expansion pattern of cities has shifted from horizontal to vertical directions, while higher urban building heights accommodate more people and their socioeconomic activities [36,37].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Urban Spatial Structure from the Perspective of Socioeconomic Benefits Based on 3D Urban Landscape Measurements: A Case Study of Beijing, China

et al. 2022

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Urban spatial structures (USS) play an essential role in urbanization. Understanding the impact of USS patterns on their socioeconomic benefits is crucial to evaluating urban structure quality. Previous studies have, primarily, relied on statistical data and have significant temporal consistency and spatial accuracy limitations. Moreover, previous evaluation methods mainly determined the weight of indicators based on subjective assessments, such as the Delphi method, without integrating the actual socioeconomic benefits of complex urban systems. By measuring the two-dimensional (2D) urban functional landscape patterns and three-dimensional (3D) building forms of the city and considering the level of urban socioeconomic vitality as revealed by nighttime light intensity (NTLI), this study explores the influence of urban spatial structure on socioeconomic vitality. It provides a new perspective for evaluating the USS level. Furthermore, a comprehensive index, namely the Spatial Structure Socioeconomic Benefit Index (SSSBI), was constructed to quantify the socioeconomic benefits of USS. The results showed that (1) the impact of spatial structure on NTLI differs significantly with the distribution of urban functional landscape patterns and building forms. (2) The combined effect of any two spatial structure factors on NTLI was higher than the effect of each factor separately, indicating that multiple dimensions can improve urban spatial construction. (3) This study quantitatively extracts the characteristics of USS from multiple scales, which helps to find the optimal evaluation scale and build a scientific and objective evaluation model. The results showed that the USS assessment based on the SSSBI index is practical. This study could provide a reference for the government’s urban planning and land-use decisions.

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“…Research on the 3D information extraction of urban buildings can serve the research fields of urban climate [2][3][4][5], urban expansion [6,7], pollutant dispersion [8], urban 3D reconstruction [9][10][11][12], urban scene classification [13], energy consumption [14], and population assessment [15][16][17]. Therefore, large-scale and high-precision 3D information extraction of urban buildings is essential for a comprehensive understanding of urban development.…”

Section: Introductionmentioning

confidence: 99%

Developing a Method to Extract Building 3D Information from GF-7 Data

Wang

Meng

et al. 2021

Remote Sensing

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The three-dimensional (3D) information of buildings can describe the horizontal and vertical development of a city. The GaoFen-7 (GF-7) stereo-mapping satellite can provide multi-view and multi-spectral satellite images, which can clearly describe the fine spatial details within urban areas, while the feasibility of extracting building 3D information from GF-7 image remains understudied. This article establishes an automated method for extracting building footprints and height information from GF-7 satellite imagery. First, we propose a multi-stage attention U-Net (MSAU-Net) architecture for building footprint extraction from multi-spectral images. Then, we generate the point cloud from the multi-view image and construct normalized digital surface model (nDSM) to represent the height of off-terrain objects. Finally, the building height is extracted from the nDSM and combined with the results of building footprints to obtain building 3D information. We select Beijing as the study area to test the proposed method, and in order to verify the building extraction ability of MSAU-Net, we choose GF-7 self-annotated building dataset and a public dataset (WuHan University (WHU) Building Dataset) for model testing, while the accuracy is evaluated in detail through comparison with other models. The results are summarized as follows: (1) In terms of building footprint extraction, our method can achieve intersection-over-union indicators of 89.31% and 80.27% for the WHU Dataset and GF-7 self-annotated datasets, respectively; these values are higher than the results of other models. (2) The root mean square between the extracted building height and the reference building height is 5.41 m, and the mean absolute error is 3.39 m. In summary, our method could be useful for accurate and automatic 3D building information extraction from GF-7 satellite images, and have good application potential.

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3d Surface Reconstruction From Multi-Date Satellite Images

Cited by 8 publications

References 30 publications

Sat-Mesh: Learning Neural Implicit Surfaces for Multi-View Satellite Reconstruction

Sat-Mesh: Learning Neural Implicit Surfaces for Multi-View Satellite Reconstruction

Evaluation of Urban Spatial Structure from the Perspective of Socioeconomic Benefits Based on 3D Urban Landscape Measurements: A Case Study of Beijing, China

Developing a Method to Extract Building 3D Information from GF-7 Data

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