Improving The Accuracy Of Digital Elevation Model Using Hopfield Neural Network With Additional Elevation Point Dataset

Q: What is DEM (Digital Elevation Model) as defined in this paper?

DEM is a crucial element in terrain analysis; however, global DEMs like SRTM and ASTER, despite having a 30m spatial resolution, exhibit limitations in accuracy over complex terrains. In this paper, DEM refers to a model with enhanced resolution and improved accuracy achieved by integrating additional elevation data using a Hopfield Neural Network (HNN) model.

Q: How does integrating additional elevation data into the Hopfield Neural Network (HNN) model affect DEM accuracy?

The research findings indicate that the HNN DEM downscaling model, which integrates additional elevation data, significantly enhances the accuracy of the DEM. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values are substantially reduced, demonstrating a 30% to 40% increase in DEM accuracy compared to existing models. This approach is particularly effective in improving the accuracy of global elevation datasets.

Nguyen, Quang Minh; La, Phu Hien; Nguyen, Thi Thu Huong; Hoang, Ngoc Ha

doi:10.7848/ksgpc.2023.41.5.395

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Type: Academic journal

Author: Nguyen, Quang Minh (Hanoi University of Mining and Geology) La, Phu Hien (Thuyloi University) Nguyen, Thi Thu Huong (Hanoi University of Mining and Geology) Hoang, Ngoc Ha (Hanoi University of Mining and Geology)

Journal: Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Vol.41 No.5

Published: 2023.10

Pages: 395 - 405 (11page)

DOI: 10.7848/ksgpc.2023.41.5.395

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In this paper, a new approach for the improvement of accuracy in DEMs (Digital Elevation Models) was proposed. While algorithms such as bilinear, bicubic, Kriging, and the HNN (Hopfield neural network) model can enhance the accuracy of DEMs, especially those derived from global data sources such as SRTM, ASTER, etc., the inclusion of additional elevation data can further improve the accuracy of the DEM. In this paper, a newly proposed resolution enhancing HNN model with the incorporation of elevation adjustment functions and variations in constraint conditions was developed and evaluated. The evaluation of the model was implemented in Cao Bang using SRTM 30m DEM data in a 1650m × 1344m area, with 130 elevation points used for accuracy enhancement and 64 points used for evaluation. The test results show an increase in accuracy of up to 40% in terms of both roots mean square error and mean absolute error when the additional elevation points were used. It has also been discovered that a zoom factor of 4 provides the best optimization in terms of balancing accuracy and computing cost for the newly proposed HNN downscaling algorithm. The results indicate that the model has the potential to be applied in practice to enhance the accuracy of DEMs, especially global DEMs after additional evaluation.

#DEM #downscaling #Hopfield Neural Network #resampling

Abstract
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2. Methods
3. Data
4. Experiment
5. Results and discussion
6. Conclusions
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