<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.</p>
</abstract>

The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Artificial Satellites

This work is licensed under the Creative Commons Attribution 4.0 International License.

The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of...

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Data publikacji: 10 paź 2023

Zakres stron: 122 - 170

Otrzymano: 06 lut 2023

Przyjęty: 09 lis 2023

DOI: https://doi.org/10.2478/arsa-2023-0009

Słowa kluczowe
digital elevation model, GIS, artificial neural network, interpolation methods, SRTM

© 2023 Mustafa K. Alemam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Data publikacji: 10 paź 2023

Zakres stron: 122 - 170

Otrzymano: 06 lut 2023

Przyjęty: 09 lis 2023

DOI: https://doi.org/10.2478/arsa-2023-0009

Słowa kluczowedigital elevation model, GIS, artificial neural network, interpolation methods, SRTM

© 2023 Mustafa K. Alemam et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
digital elevation model, GIS, artificial neural network, interpolation methods, SRTM