The study proposes a fusion strategy using TV-norm regularization and ANN-predicted weights to combine TanDEM-X and Cartosat-1 DEMs, which significantly improves the final DEM quality over urban areas.

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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.

The study proposes a fusion strategy using TV-norm regularization and ANN-predicted weights to combine TanDEM-X and Cartosat-1 DEMs, which significantly improves the final DEM quality over urban areas.

Fusion of TanDEM-X and Cartosat-1 DEMS using TV-norm regularization and ANN-predicted weights