@inproceedings{414e46c372334cfba833eb045b148219,
title = "Fusion of TanDEM-X and Cartosat-1 DEMS using TV-norm regularization and ANN-predicted weights",
abstract = "This paper deals with TanDEM-X and Cartosat-1 DEM fusion over urban areas with support of weight maps predicted by an artificial neural network (ANN). Although the TanDEM-X DEM is a global elevation dataset of unprecedented accuracy (following HRTI-3 standard), its quality decreases over urban areas because of artifacts intrinsic to the SAR imaging geometry. DEM fusion techniques can be used to improve the TanDEM-X DEM in problematic areas. In this investigation, Cartosat-1 elevation data were fused with the TanDEM-X DEM by weighted averaging and total variation (TV)-based regularization, resorting to weight maps derived by a specifically trained ANN. The results show that the proposed fusion strategy can significantly improve the final DEM quality.",
keywords = "Artificial Neural Network, Cartosat-1 DEM, Data fusion, L norm total variation, TanDEM-X DEM, weight map",
author = "H. Bagheri and M. Schmitt and Zhu, {X. X.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8127720",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3369--3372",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}