Abstract
High quality, accessible Digital Elevation Model (DEM) datasets play a major role in monitoring the changes in the Earth’s surface. This study proposes a novel method to increase the vertical precision of CARTOSAT 10 m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods. Machine learning methods such as Genetic Programming (GP) and Artificial Neural Networks (ANN) are applied to the SRTM-1 DEM and the CARTOSAT DEM in India to generate DEM of improved vertical accuracy. Quantifiable results show that proposed approach improve the vertical accuracy, considering the reference as Ground control Points (GCPs) elevation from Differential Global Positioning System (DGPS) survey data. Significant improvements of 47 and 35% in RMSE are offered by generated DEMs compared to the SRTM-1 and CARTOSAT respectively.
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Acknowledgments
Dr. R. Maheswaran gratefully acknowledges the funding received from the Department of Science and Technology, Water Technology Initiative under the project DST/WTI/DD/2k17/0079. The authors also acknowledge Surveycon Ltd. Visakhapatnam for providing the DGPS survey data used in this study.
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Communicated by: H. Babaie
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Kasi, V., Yeditha, P., Rathinasamy, M. et al. A novel method to improve vertical accuracy of CARTOSAT DEM using machine learning models. Earth Sci Inform 13, 1139–1150 (2020). https://doi.org/10.1007/s12145-020-00494-1
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DOI: https://doi.org/10.1007/s12145-020-00494-1