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Article

Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

1
Tropical Marine Science Institute, National University of Singapore, Singapore 119077, Singapore
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Willis Research Network, Willis Towers Watson, 51 Lime Street, London X0 EC3M 7DQ, UK
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Polytech Lab, Polytech Nice Sophia, Université Côte d’Azur, 930 Route des Colles, 06903 Sophia Antipolis, France
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German Aerospace Center (DLR), Earth Observation Center, Oberpfaffenhofen, 82234 Weßling, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Juraj Parajka and Fi-John Chang
Water 2021, 13(11), 1551; https://doi.org/10.3390/w13111551
Received: 29 March 2021 / Revised: 24 May 2021 / Accepted: 27 May 2021 / Published: 31 May 2021
(This article belongs to the Section Hydrology)
The digital elevation model (DEM) is crucial for various applications, such as land management and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface. However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly, and often confidential. This paper explores a DEM improvement scheme using an artificial neural network (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). The ANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and then applied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-X data of the Nice area were used as the input data, while the ground truth observation data of Nice were used as the target data. The applicability of iTanDEM-X was finally conducted at a different site in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean square error of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g., vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the application of the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available; for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings). View Full-Text
Keywords: artificial neural network; digital elevation model; remote sensing artificial neural network; digital elevation model; remote sensing
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MDPI and ACS Style

Kim, D.E.; Liu, J.; Liong, S.-Y.; Gourbesville, P.; Strunz, G. Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network. Water 2021, 13, 1551. https://doi.org/10.3390/w13111551

AMA Style

Kim DE, Liu J, Liong S-Y, Gourbesville P, Strunz G. Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network. Water. 2021; 13(11):1551. https://doi.org/10.3390/w13111551

Chicago/Turabian Style

Kim, Dong E., Jiandong Liu, Shie-Yui Liong, Philippe Gourbesville, and Günter Strunz. 2021. "Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network" Water 13, no. 11: 1551. https://doi.org/10.3390/w13111551

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