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논문 기본 정보

자료유형
학술저널
저자정보
(Hanoi University of Mining and Geology) (Thuyloi University) (Hanoi University of Mining and Geology) (Hanoi University of Mining and Geology)
저널정보
한국측량학회 한국측량학회지 한국측량학회지 제41권 제5호
발행연도
수록면
395 - 405 (11page)
DOI
10.7848/ksgpc.2023.41.5.395

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초록· 키워드

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
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목차

  1. Abstract
  2. 1. Introduction
  3. 2. Methods
  4. 3. Data
  5. 4. Experiment
  6. 5. Results and discussion
  7. 6. Conclusions
  8. References

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