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Uncertainty assessment and weight map generation for efficient fusion of TanDEM-X and Cartosat-1 DEMs

Bagheri, Hossein and Schmitt, Michael and Zhu, Xiao Xiang (2017) Uncertainty assessment and weight map generation for efficient fusion of TanDEM-X and Cartosat-1 DEMs. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences pp. 433-439.

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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.

Item URL in elib:https://elib.dlr.de/116074/
Document Type:Contribution to a Collection
Title:Uncertainty assessment and weight map generation for efficient fusion of TanDEM-X and Cartosat-1 DEMs
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bagheri, Hosseintu münchenUNSPECIFIED
Schmitt, Michaelm.schmitt (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangdlr-imf/tum-lmfUNSPECIFIED
Date:2017
Journal or Publication Title:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:42
Page Range:pp. 433-439
Status:Published
Keywords:Accuracy assessment, Data fusion, Predicted weight map, Artificial Neural Network, TanDEM-X DEM, Cartosat-1 DEM
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Häberle, Matthias
Deposited On:06 Dec 2017 13:10
Last Modified:31 Jul 2019 20:13

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