Very High Resolution Mapping with the Pleiades Satellite Constellation

Roland Perko*, Hannes Raggam, Mathias Schardt, Peter M. Roth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)89-99
Number of pages11
JournalAmerican Journal of Remote Sensing
Issue number2
Publication statusPublished - Dec 2018

Fields of Expertise

  • Sustainable Systems

Cite this