A comparison of stereo and multiview 3-D reconstruction using cross-sensor satellite imagery

Ozcanli, Ozge C.; Dong, Yi; Mundy, Joseph L.; Webb, Helen; Hammoud, Riad I.; Tom, Victor T.

doi:10.1109/cvprw.2015.7301292

Cited by 30 publications

(19 citation statements)

References 14 publications

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“…For these arid and semi-arid areas, different image combinations were evaluated, and guidelines for image selection were proposed. Multi-date and sensor image matching using pairwise matching and multiview matching is evaluated by Ozcanli et al (2015). Pairwise SGM (Hirschmüller, 2008) dense-matching followed by median DSM fusion is shown to provide the best results.…”

Section: Previous Workmentioning

confidence: 99%

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Cross-track satellite stereo for 3D modelling of urban areas

Krauss

d’Angelo

Wendt

2018

European Journal of Remote Sensing

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Stereo imagery from very high-resolution satellite series can be used to create high-resolution digital surface models (DSMs) with sub-meter spatial resolution. Especially when in-orbit tristereo acquisitions are available as provided by the Pléiades constellation, buildings can be reconstructed well even in dense city centres. Unfortunately, such high-quality stereo data are often not available in the image archives and, thus, need to be newly acquired in most cases, leading to long delays and high cost. Especially for rapid mapping or crisis-response applications, high-resolution DSMs would be helpful for tasks such as city modelling and ultimately population estimation. In this work, we test the feasibility of producing DSMs by pairing two archive images, which were not acquired on the same day, in the same orbit or by the same satellite. We tested this approach for 14 images from Port-au-Prince, Haiti, and 12 images from Salzburg, Austria, from which we calculated a total of 136 DSMs. These were evaluated against Lidar DSMs. The resulting DSMs reach a completeness of up to 60%, and partly Median Absolute Deviation values comparable to in-orbit stereo pairs. For best results, the stereo convergence angle should be between 5 and 15°, and the difference in sun angles between the two images should be below 25-30.

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Section: Previous Workmentioning

confidence: 99%

“…One way to improve the quality of cross-track data is to merge the results of multiple stereo pairs (Ozcanli et al, 2015). This leads to dense DSMs with good completeness and accuracy values.…”

Section: Merging Of Cross-track Datasetsmentioning

confidence: 99%

Cross-track satellite stereo for 3D modelling of urban areas

Krauss

d’Angelo

Wendt

2018

European Journal of Remote Sensing

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“…MVS methods are the most popular choice for satellite imagery, as it has been shown that they can outperform sophisticated true multi-view techniques [21]. The efficient and stable performance of the semi-global matching (SGM) algorithm [16] typically used in stereo pipelines (e.g.…”

Section: Introductionmentioning

confidence: 99%

To Bundle Adjust or Not: A Comparison of Relative Geolocation Correction Strategies for Satellite Multi-View Stereo

Marí

Franchis²,

Meinhardt-Llopis³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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The generation of up-to-date accurate 3D models from multi-view satellite images has recently become a hot research topic. A well-known challenge of this problem is to put all cameras into a common frame of reference, since depending on the satellite geopositioning equipment the camera parameters may contain errors of up to tens of meters on the ground. In this context, bundle adjustment based techniques, relying on the identification of a set of tie-points and the correction of the camera models to make them coincident, have become a generally accepted practice. However, new approaches capable of producing state-of-the-art results without the use of prior bundle adjustment have also been proposed. This work aims to compare both strategies and assess the practical impact of using bundle adjustment for 3D reconstruction from multi-view satellite images.

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“…The true multi-view method is more rigorous but more complicated. Some researchers have investigated and compared both 3D reconstruction methods on satellite MVS images (Ozcanli et al, 2015). In their implementation, the pair-wise multi-view reconstruction method generates better results than the other method.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud and Digital Surface Model Generation From High Resolution Multiple View Stereo Satellite Imagery

Gong

Fritsch

2018

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

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ABSTRACT:Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs' generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust.

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A comparison of stereo and multiview 3-D reconstruction using cross-sensor satellite imagery

Cited by 30 publications

References 14 publications

Cross-track satellite stereo for 3D modelling of urban areas

Cross-track satellite stereo for 3D modelling of urban areas

To Bundle Adjust or Not: A Comparison of Relative Geolocation Correction Strategies for Satellite Multi-View Stereo

Point Cloud and Digital Surface Model Generation From High Resolution Multiple View Stereo Satellite Imagery

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