Very High Resolution Mapping with the Pléiades Satellite Constellation

Roland Perko; Hannes Raggam; Mathias Schardt; Peter Michael Roth

doi:doi:10.11648/j.ajrs.20180602.14

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Very High Resolution Mapping with the Pléiades Satellite Constellation

Roland Perko, Hannes Raggam, Mathias Schardt, Peter Michael Roth

Received: 16 August 2018 Accepted: 30 November 2018 Published: 24 December 2018

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Abstract

The Pléiades satellite constellation provides very high resolution multi-spectral optical data at a ground sampling distance of about 0.7 m at nadir direction. Due to the highly agile pointing angle capacity in the range of ±47 degrees the sensors are optimal for detailed earth observation. They are able to collect stereo and tri-stereo datasets in one overflight with a swath width of 20 km. Such images allow 3D mapping of any region on the Earth’s surface at very high resolution with high accuracy, where the reconstruction of the heights is based on along-track stereo. This work presents methodologies and workflows within the fields of remote sensing and computer vision that are used (1) to densely reconstruct digital surface models (DSM), (2) to derive digital terrain models (DTM), and (3) to generate multi-spectral ortho-rectified products. Within this process, the accuracy of the geometric sensor models, given as rational polynomial coefficient (RPC) models, plays a crucial role. Therefore, an assessment is performed on two distinct test sites discussing the initial 2D geo-location accuracy of the given sensor models. An optimization scheme is presented to adjust the given RPC models yielding 3D geo-location accuracies of 0.5 m in planimetry and 1 m in height. In the frame of surface model generation important issues like epipolar rectification, hierarchical stereo matching, and fusion of heights are reported. The main outcomes are that the sensor accuracy is within the range as defined by Astrium, but that a sensor model optimization is obligatory when it comes to highly accurate 3D mapping. The presented workflow generates mapping products with a GSD of 0.5m. The derived DSMs and DTMs show a high level of detail, thus enabling varying applications on a large scale, like land cover and land use classification, change detection, city modelling, or forest assessment.

DOI	10.11648/j.ajrs.20180602.14
Published in	American Journal of Remote Sensing (Volume 6, Issue 2, December 2018)
Page(s)	89-99
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Pléiades, Sensor Model, Accuracy Analysis, 3D Mapping, Digital Surface Model, Digital Terrain Model

References

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APA Style

Roland Perko, Hannes Raggam, Mathias Schardt, Peter Michael Roth. (2018). Very High Resolution Mapping with the Pléiades Satellite Constellation. American Journal of Remote Sensing, 6(2), 89-99. https://doi.org/10.11648/j.ajrs.20180602.14

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ACS Style

Roland Perko; Hannes Raggam; Mathias Schardt; Peter Michael Roth. Very High Resolution Mapping with the Pléiades Satellite Constellation. Am. J. Remote Sens. 2018, 6(2), 89-99. doi: 10.11648/j.ajrs.20180602.14

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AMA Style

Roland Perko, Hannes Raggam, Mathias Schardt, Peter Michael Roth. Very High Resolution Mapping with the Pléiades Satellite Constellation. Am J Remote Sens. 2018;6(2):89-99. doi: 10.11648/j.ajrs.20180602.14

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@article{10.11648/j.ajrs.20180602.14,
  author = {Roland Perko and Hannes Raggam and Mathias Schardt and Peter Michael Roth},
  title = {Very High Resolution Mapping with the Pléiades Satellite Constellation},
  journal = {American Journal of Remote Sensing},
  volume = {6},
  number = {2},
  pages = {89-99},
  doi = {10.11648/j.ajrs.20180602.14},
  url = {https://doi.org/10.11648/j.ajrs.20180602.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20180602.14},
  abstract = {The Pléiades satellite constellation provides very high resolution multi-spectral optical data at a ground sampling distance of about 0.7 m at nadir direction. Due to the highly agile pointing angle capacity in the range of ±47 degrees the sensors are optimal for detailed earth observation. They are able to collect stereo and tri-stereo datasets in one overflight with a swath width of 20 km. Such images allow 3D mapping of any region on the Earth’s surface at very high resolution with high accuracy, where the reconstruction of the heights is based on along-track stereo. This work presents methodologies and workflows within the fields of remote sensing and computer vision that are used (1) to densely reconstruct digital surface models (DSM), (2) to derive digital terrain models (DTM), and (3) to generate multi-spectral ortho-rectified products. Within this process, the accuracy of the geometric sensor models, given as rational polynomial coefficient (RPC) models, plays a crucial role. Therefore, an assessment is performed on two distinct test sites discussing the initial 2D geo-location accuracy of the given sensor models. An optimization scheme is presented to adjust the given RPC models yielding 3D geo-location accuracies of 0.5 m in planimetry and 1 m in height. In the frame of surface model generation important issues like epipolar rectification, hierarchical stereo matching, and fusion of heights are reported. The main outcomes are that the sensor accuracy is within the range as defined by Astrium, but that a sensor model optimization is obligatory when it comes to highly accurate 3D mapping. The presented workflow generates mapping products with a GSD of 0.5m. The derived DSMs and DTMs show a high level of detail, thus enabling varying applications on a large scale, like land cover and land use classification, change detection, city modelling, or forest assessment.},
 year = {2018}
}

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TY  - JOUR
T1  - Very High Resolution Mapping with the Pléiades Satellite Constellation
AU  - Roland Perko
AU  - Hannes Raggam
AU  - Mathias Schardt
AU  - Peter Michael Roth
Y1  - 2018/12/24
PY  - 2018
N1  - https://doi.org/10.11648/j.ajrs.20180602.14
DO  - 10.11648/j.ajrs.20180602.14
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 89
EP  - 99
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20180602.14
AB  - The Pléiades satellite constellation provides very high resolution multi-spectral optical data at a ground sampling distance of about 0.7 m at nadir direction. Due to the highly agile pointing angle capacity in the range of ±47 degrees the sensors are optimal for detailed earth observation. They are able to collect stereo and tri-stereo datasets in one overflight with a swath width of 20 km. Such images allow 3D mapping of any region on the Earth’s surface at very high resolution with high accuracy, where the reconstruction of the heights is based on along-track stereo. This work presents methodologies and workflows within the fields of remote sensing and computer vision that are used (1) to densely reconstruct digital surface models (DSM), (2) to derive digital terrain models (DTM), and (3) to generate multi-spectral ortho-rectified products. Within this process, the accuracy of the geometric sensor models, given as rational polynomial coefficient (RPC) models, plays a crucial role. Therefore, an assessment is performed on two distinct test sites discussing the initial 2D geo-location accuracy of the given sensor models. An optimization scheme is presented to adjust the given RPC models yielding 3D geo-location accuracies of 0.5 m in planimetry and 1 m in height. In the frame of surface model generation important issues like epipolar rectification, hierarchical stereo matching, and fusion of heights are reported. The main outcomes are that the sensor accuracy is within the range as defined by Astrium, but that a sensor model optimization is obligatory when it comes to highly accurate 3D mapping. The presented workflow generates mapping products with a GSD of 0.5m. The derived DSMs and DTMs show a high level of detail, thus enabling varying applications on a large scale, like land cover and land use classification, change detection, city modelling, or forest assessment.
VL  - 6
IS  - 2
ER  -

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Author Information

Roland Perko

Institute for Information and Communication Technologies, Joanneum Research, Graz, Austria
Hannes Raggam

Institute for Information and Communication Technologies, Joanneum Research, Graz, Austria
Mathias Schardt

Institute for Information and Communication Technologies, Joanneum Research, Graz, Austria
Peter Michael Roth

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria

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APA Style

Roland Perko, Hannes Raggam, Mathias Schardt, Peter Michael Roth. (2018). Very High Resolution Mapping with the Pléiades Satellite Constellation. American Journal of Remote Sensing, 6(2), 89-99. https://doi.org/10.11648/j.ajrs.20180602.14

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ACS Style

Roland Perko; Hannes Raggam; Mathias Schardt; Peter Michael Roth. Very High Resolution Mapping with the Pléiades Satellite Constellation. Am. J. Remote Sens. 2018, 6(2), 89-99. doi: 10.11648/j.ajrs.20180602.14

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AMA Style

Roland Perko, Hannes Raggam, Mathias Schardt, Peter Michael Roth. Very High Resolution Mapping with the Pléiades Satellite Constellation. Am J Remote Sens. 2018;6(2):89-99. doi: 10.11648/j.ajrs.20180602.14

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@article{10.11648/j.ajrs.20180602.14,
  author = {Roland Perko and Hannes Raggam and Mathias Schardt and Peter Michael Roth},
  title = {Very High Resolution Mapping with the Pléiades Satellite Constellation},
  journal = {American Journal of Remote Sensing},
  volume = {6},
  number = {2},
  pages = {89-99},
  doi = {10.11648/j.ajrs.20180602.14},
  url = {https://doi.org/10.11648/j.ajrs.20180602.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20180602.14},
  abstract = {The Pléiades satellite constellation provides very high resolution multi-spectral optical data at a ground sampling distance of about 0.7 m at nadir direction. Due to the highly agile pointing angle capacity in the range of ±47 degrees the sensors are optimal for detailed earth observation. They are able to collect stereo and tri-stereo datasets in one overflight with a swath width of 20 km. Such images allow 3D mapping of any region on the Earth’s surface at very high resolution with high accuracy, where the reconstruction of the heights is based on along-track stereo. This work presents methodologies and workflows within the fields of remote sensing and computer vision that are used (1) to densely reconstruct digital surface models (DSM), (2) to derive digital terrain models (DTM), and (3) to generate multi-spectral ortho-rectified products. Within this process, the accuracy of the geometric sensor models, given as rational polynomial coefficient (RPC) models, plays a crucial role. Therefore, an assessment is performed on two distinct test sites discussing the initial 2D geo-location accuracy of the given sensor models. An optimization scheme is presented to adjust the given RPC models yielding 3D geo-location accuracies of 0.5 m in planimetry and 1 m in height. In the frame of surface model generation important issues like epipolar rectification, hierarchical stereo matching, and fusion of heights are reported. The main outcomes are that the sensor accuracy is within the range as defined by Astrium, but that a sensor model optimization is obligatory when it comes to highly accurate 3D mapping. The presented workflow generates mapping products with a GSD of 0.5m. The derived DSMs and DTMs show a high level of detail, thus enabling varying applications on a large scale, like land cover and land use classification, change detection, city modelling, or forest assessment.},
 year = {2018}
}

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TY  - JOUR
T1  - Very High Resolution Mapping with the Pléiades Satellite Constellation
AU  - Roland Perko
AU  - Hannes Raggam
AU  - Mathias Schardt
AU  - Peter Michael Roth
Y1  - 2018/12/24
PY  - 2018
N1  - https://doi.org/10.11648/j.ajrs.20180602.14
DO  - 10.11648/j.ajrs.20180602.14
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 89
EP  - 99
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20180602.14
AB  - The Pléiades satellite constellation provides very high resolution multi-spectral optical data at a ground sampling distance of about 0.7 m at nadir direction. Due to the highly agile pointing angle capacity in the range of ±47 degrees the sensors are optimal for detailed earth observation. They are able to collect stereo and tri-stereo datasets in one overflight with a swath width of 20 km. Such images allow 3D mapping of any region on the Earth’s surface at very high resolution with high accuracy, where the reconstruction of the heights is based on along-track stereo. This work presents methodologies and workflows within the fields of remote sensing and computer vision that are used (1) to densely reconstruct digital surface models (DSM), (2) to derive digital terrain models (DTM), and (3) to generate multi-spectral ortho-rectified products. Within this process, the accuracy of the geometric sensor models, given as rational polynomial coefficient (RPC) models, plays a crucial role. Therefore, an assessment is performed on two distinct test sites discussing the initial 2D geo-location accuracy of the given sensor models. An optimization scheme is presented to adjust the given RPC models yielding 3D geo-location accuracies of 0.5 m in planimetry and 1 m in height. In the frame of surface model generation important issues like epipolar rectification, hierarchical stereo matching, and fusion of heights are reported. The main outcomes are that the sensor accuracy is within the range as defined by Astrium, but that a sensor model optimization is obligatory when it comes to highly accurate 3D mapping. The presented workflow generates mapping products with a GSD of 0.5m. The derived DSMs and DTMs show a high level of detail, thus enabling varying applications on a large scale, like land cover and land use classification, change detection, city modelling, or forest assessment.
VL  - 6
IS  - 2
ER  -

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