Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 433-439, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 433-439, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

UNCERTAINTY ASSESSMENT AND WEIGHT MAP GENERATION FOR EFFICIENT FUSION OF TANDEM-X AND CARTOSAT-1 DEMS

H. Bagheri1, M. Schmitt1, and X. X. Zhu1,2 H. Bagheri et al.
  • 1Signal Processing in Earth Observation, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany
  • 2Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany

Keywords: Accuracy assessment, Data fusion, Predicted weight map, Artificial Neural Network, TanDEM-X DEM, Cartosat-1 DEM

Abstract. Recently, with InSAR data provided by the German TanDEM-X mission, a new global, high-resolution Digital Elevation Model (DEM) has been produced by the German Aerospace Center (DLR) with unprecedented height accuracy. However, due to SAR-inherent sensor specifics, its quality decreases over urban areas, making additional improvement necessary. On the other hand, DEMs derived from optical remote sensing imagery, such as Cartosat-1 data, have an apparently greater resolution in urban areas, making their fusion with TanDEM-X elevation data a promising perspective. The objective of this paper is two-fold: First, the height accuracies of TanDEM-X and Cartosat-1 elevation data over different land types are empirically evaluated in order to analyze the potential of TanDEM-XCartosat- 1 DEM data fusion. After the quality assessment, urban DEM fusion using weighted averaging is investigated. In this experiment, both weight maps derived from the height error maps delivered with the DEM data, as well as more sophisticated weight maps predicted by a procedure based on artificial neural networks (ANNs) are compared. The ANN framework employs several features that can describe the height residual performance to predict the weights used in the subsequent fusion step. The results demonstrate that especially the ANN-based framework is able to improve the quality of the final DEM through data fusion.