Mapping with Pleiades

Publikation aus Digital

Roland Perko

Master's thesis , 1/2019


An end-to-end workflow for mapping with very high resolution satellite data is the pre-requisite for any further semantic analysis. In specific, many applications in remote sensing need the following 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model, and (4) ortho-rectified image mosaic. This thesis describes all underlying principles for satellite-based 3D mapping and proposes methods that extract all those products from multi-view stereo satellite imagery in the ground sampling distance of the input data. The study is based on, but not limited to, the Pleiades satellite constellation. Beside an in-depth review of related work, the methodological part proposes solutions for each component of the end-to-end workflow. In particular, this includes 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 step implementation details are proposed and discussed. Another aim of this thesis is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pleiades 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 meters in planimetry and 0.2 to 0.4 meters in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around 0.9 meters in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models.

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