3D reconstruction from multi-view VHR-satellite images in MicMac

Rupnik, Ewelina; Pierrot-Deseilligny, Marc; Delorme, Arthur

doi:10.1016/j.isprsjprs.2018.03.016

Cited by 65 publications

(47 citation statements)

References 17 publications

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“…To our best knowledge, volume change measurements were never computed from a large number of VHR satellite stereo-images (>10), but studies suggest that the combination of multi-view images can improve the DEMs quality. The fusion of 16 Worldview-3 images improved the NMAD of the residual by 20% compared to a set of 6 images over an industrial zone (Rupnik et al, 2018). Therefore, the most important use of tri-stereo may not be to improve the accuracy of HS maps, but rather to obtain complete coverage of complex terrains and have a less distorted nadir ortho-image for the land surface classification.…”

Section: Sensitivity To Image Geometry and Photogrammetric Processingmentioning

confidence: 99%

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

Deschamps-Berger¹,

Gascoin²,

Deems³

et al. 2020

Preprint

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Abstract. An accurate knowledge of snow depth distribution in mountain catchments is critical for applications in hydrology and ecology. A recent new method was proposed to map the snow depth at meter-scale resolution from very-high resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.50 m. However, the validation was mainly done using probe measurements which sampled a limited fraction of the topographic and snow depth variability. We deepen this evaluation using accurate maps of the snow depth derived from ASO airborne lidar measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 137 km2 on a 3 m grid with a positive bias for Pléiades snow depth of 0.08 m, a root-mean-square error of 0.80 m and a normalized median absolute deviation of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale, and also small-scale features like snow drifts and avalanche deposits. The random error on snow depth can be reduced by a factor two (up to approximately 0.40 m) when the snow depth map is spatially averaged to a ~ 20 m grid. The random error at the pixel level is lower on snow-free areas than on snow-covered areas, but errors on both terrain type converge at coarser resolutions, which is important for further applications of the method in areas without snow depth reference data. We conclude that satellite photogrammetry stands out as an efficient method to estimate the spatial distribution of snow depth in high mountain catchments.

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Section: Sensitivity To Image Geometry and Photogrammetric Processingmentioning

confidence: 99%

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

Deschamps-Berger¹,

Gascoin²,

Deems³

et al. 2020

Preprint

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“…Moreover, it is computationally very efficient. Currently many commercial packages for processing satellite data use epipolar rectification and SGM for DSM generation, for example, Erdas Imagine (http://www.hexagongeospatial.com/products/remote-sensing/erdas-imagine/overview), PCI Geomatica (http://www.pcigeomatics.com), Trimble Inpho with Match-T or MATCH-3DX (http://www.trimble.com/imaging/inpho.aspx), s2p (http://dev.ipol.im/~carlo/s2p/), SURE (http: //nframes.com/) (only works with perspective images [65]), MicMac (http://micmac.ensg.eu) [66], RPC Stereo Processor (http://u.osu.edu/qin.324/rsp/) [67] and also the Remote Sensing Software Graz (http://www.remotesensing.at/en/remote-sensing-software.html). Recent research reports on different variants of SGM, such as Reference [65] and extensions like those in References [68][69][70][71].…”

Section: Stereo Matchingmentioning

confidence: 99%

Mapping with Pléiades—End-to-End Workflow

2019

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In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe all underlying principles for satellite-based 3D mapping and propose methods that extract these products from multi-view stereo satellite imagery. Our workflow is demonstrated for the Pléiades satellite constellation, however, the applied building blocks are more general and thus also applicable for different setups. Besides introducing the overall end-to-end workflow, we need also to tackle single building blocks: optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, digital terrain model derivation, ortho rectification and ortho mosaicing. For each of these steps, extensions to the state-of-the-art are proposed and discussed in detail. In addition, a novel approach for terrain model generation is introduced. The second aim of the study is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pléiades images. First, the accuracies of the 2D and 3D geo-location are analyzed. Second, surface and terrain models are evaluated, including a critical look on the underlying error metrics and discussing the differences of single stereo, tri-stereo and multi-view data sets. Overall, 3D accuracies in the range of 0.2 to 0.3 m in planimetry and 0.2 to 0.4 m in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around 0.9 m in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models.then results in 3D coordinates (i.e., a 3D point cloud) that is resampled into an intermediate DSM. Second, those DSMs are fused and post-processed yielding the final DSM. Within this task, the characteristics of the underlying sensor system is exploited, like availability of image triplets in case of Pléiadesand including high-level processing techniques for the given input. The applicability of high-level processing options, like the semi-global matching (SGM) technique, is investigated, enhanced and resulting achievements are illustrated and validated. 3.In the third task, one DTM and one nDSM are generated solely based on the DSM produced in the previous task. The main steps are ground point filtering, detecting points in the given DSM which are located on bare earth, hole filling to interpolate non-ground information and post-processing mainly for outlier removal. In this way, we can (a) filter man-made structures and vegetation and (b) retrieve vegetation or building heights, finally yielding the DTM and nDSM models. While this is a well-established procedure for airborne light dete...

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“…Some authors [10] already noted that the accuracy and completeness of models in urban areas are lower. Rupnik et al [11] applied a multi-view reconstruction algorithm on an urban area and reported some differences in quality indicators as a consequence of varying housing density. In this context, the geometry of acquisition appears to influence the quality of the final results [12].…”

Section: Introductionmentioning

confidence: 99%

Metric Accuracy of Digital Elevation Models from WorldView-3 Stereo-Pairs in Urban Areas

2019

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WorldView-3 satellite is providing images with an unprecedented combination of high spatial and spectral resolution. The stereo capabilities and the very high resolution of the panchromatic band (0.31 m) have been fostering new applications in urban areas, where the complexity of the morphology requires a higher level of detail. The present technical note aims to test the accuracy of digital elevation models that can be obtained by WorldView-3 stereo-pairs in these particular contexts, with an operational state-of-the-art algorithm. Validation is performed using check points and existing models of the area (from LiDAR data and oblique aerial images). The experiments, conducted over the city of Bologna (Italy) with six images, proved that roof surfaces and open spaces can be reconstructed with an average error of 1–2 pixels, but severe discrepancies frequently occur in narrow roads and urban canyons (up to several metres in average). The level of completeness achievable with only one pair is extremely variable (ranging from 50% to 90%), due to the combined effect of the geometry of acquisition and the specific urban texture. Better results can be obtained by using more than one pair. Furthermore, smaller convergence angles can be beneficial for the reconstruction of specific urban structures, such as soaring towers.

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3D reconstruction from multi-view VHR-satellite images in MicMac

Cited by 65 publications

References 17 publications

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

Mapping with Pléiades—End-to-End Workflow

Metric Accuracy of Digital Elevation Models from WorldView-3 Stereo-Pairs in Urban Areas

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