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

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

Open Access
|Oct 2023

Figures & Tables

Figure 1.

ANN architecture
ANN architecture

Figure 2.

First method
First method

Figure 3.

Last method
Last method

Figure 4.

Minimum method
Minimum method

Figure 5.

Maximum method
Maximum method

Figure 6.

Mean method
Mean method

Figure 7.

Blend method
Blend method

Figure 8.

Study area and the reference point R
Study area and the reference point R

Figure 9.

Methodology flow chart
Methodology flow chart

Figure 10.

The two cases of the study area border
The two cases of the study area border

Figure 11.

DEMGNSS for the study area
DEMGNSS for the study area

Figure 12.

SRTM DEM for the study area
SRTM DEM for the study area

Figure 13.

DEM of the overlap area
DEM of the overlap area

Figure 14.

The position of overlap area on SRTM DEM
The position of overlap area on SRTM DEM

Figure 15.

Algorithm steps of the ANN stage
Algorithm steps of the ANN stage

Figure 16.

Box and whisker plot elements
Box and whisker plot elements

Figure 17.

Elevation differences between DEMSRTM and DEMGNSS
Elevation differences between DEMSRTM and DEMGNSS

Figure 18.

Elevation differences between the conventional DEMs merging methods and DEMGNSS
Elevation differences between the conventional DEMs merging methods and DEMGNSS

Figure 19.

Root mean square error of the elevation’s differences between DEMGNSS and the merged DEMs in the case of conventional DEMs merging methods
Root mean square error of the elevation’s differences between DEMGNSS and the merged DEMs in the case of conventional DEMs merging methods

Figure 20.

Elevation differences according to DEMGNSS for the proposed DEM merging methods in the case of zero border and the ANN
Elevation differences according to DEMGNSS for the proposed DEM merging methods in the case of zero border and the ANN

Figure 21.

Errors in elevation between the DEMGNSS and the proposed DEM merging methods in the case of additional observations at the study area border
Errors in elevation between the DEMGNSS and the proposed DEM merging methods in the case of additional observations at the study area border

Figure 22.

Root mean square error of the elevations differences between DEMGNSS and the merged DEMs in the case of PDMMs
Root mean square error of the elevations differences between DEMGNSS and the merged DEMs in the case of PDMMs

Figure 23.

Classification of the difference between DEMGNSS and DEMSRTM into four categories of accuracy
Classification of the difference between DEMGNSS and DEMSRTM into four categories of accuracy

Figure 24.

Four categories of the merged DEMs accuracy resulting from the differences between DEMGNSS and the merged DEMs in the case of CDMMs
Four categories of the merged DEMs accuracy resulting from the differences between DEMGNSS and the merged DEMs in the case of CDMMs

Figure 25.

Classification of the elevations differences between DEMGNSS and the PDMMs in the case of zeros border and ANN
Classification of the elevations differences between DEMGNSS and the PDMMs in the case of zeros border and ANN

Figure 26.

Four categories of the merged DEMs accuracy resulting from the differences between DEMGNSS and merged DEMs by PDMMs in the case of additional observations at the border
Four categories of the merged DEMs accuracy resulting from the differences between DEMGNSS and merged DEMs by PDMMs in the case of additional observations at the border

Figure 27.

DEMs of the slopes and aspects for the GNSS points
DEMs of the slopes and aspects for the GNSS points

Figure 28.

DEMs of the slopes and aspects for the SRTM data
DEMs of the slopes and aspects for the SRTM data

Figure 29.

Slopes distribution of the conventional DEMs merging methods
Slopes distribution of the conventional DEMs merging methods

Figure 30.

Aspects distribution of the conventional DEMs merging methods
Aspects distribution of the conventional DEMs merging methods

Figure 31.

Root mean square error of the slope differences between DEMGNSS and the merged DEMs in the case of conventional DEMs merging methods
Root mean square error of the slope differences between DEMGNSS and the merged DEMs in the case of conventional DEMs merging methods

Figure 32.

Root mean square error of the aspects differences between DEMGNSS and the merged DEMs in the case of CDMMs
Root mean square error of the aspects differences between DEMGNSS and the merged DEMs in the case of CDMMs

Figure 33.

Slopes distribution of the proposed DEMs merging methods in the case of zeros border and the ANN
Slopes distribution of the proposed DEMs merging methods in the case of zeros border and the ANN

Figure 34.

Slopes distribution of the proposed DEMs merging methods in the case of additional observations at the study area border
Slopes distribution of the proposed DEMs merging methods in the case of additional observations at the study area border

Figure 35.

Aspects distribution of the proposed DEMs merging methods in the case of zeros border and the ANN
Aspects distribution of the proposed DEMs merging methods in the case of zeros border and the ANN

Figure 36.

Aspects distribution of the proposed DEMs merging methods in the case of additional observations at the study area border
Aspects distribution of the proposed DEMs merging methods in the case of additional observations at the study area border

Figure 37.

Root mean square error of the slope differences between DEMGNSS and the merged DEMs in the case of PDMMs
Root mean square error of the slope differences between DEMGNSS and the merged DEMs in the case of PDMMs

Figure 38.

Root mean square error of the aspects differences between DEMGNSS and the merged DEMs in the case of PDMMs
Root mean square error of the aspects differences between DEMGNSS and the merged DEMs in the case of PDMMs

Figure 39.

The improvement of elevations, slopes, and aspects by the conventional DEMs merging methods compared to DEMSRTM
The improvement of elevations, slopes, and aspects by the conventional DEMs merging methods compared to DEMSRTM

Figure 40.

The improvement of elevations, slopes, and aspects by the proposed DEMs merging methods in comparison with DEMSRTM
The improvement of elevations, slopes, and aspects by the proposed DEMs merging methods in comparison with DEMSRTM

The differences in elevations between the DEMSRTM and DEMGNSS

DEM typeAbsolute values statisticsBox chart statistics
MAEMax.Min.RMSEMedianWhisker (max.)Whisker (min.)25th percentile75th percentile
DEMSRTM3.6419.490.004.78-2.473.37-8.68-4.16-1.15

The elevation differences between the conventional DEMs merging methods and the DEMGNSS

Merged DEMsAbsolute values statisticsBox chart statistics
MAEMax.Min.RMSEMedianWhisker (max.)Whisker (min.)25th percentile75th percentile
MDEMFirst3.2619.640.004.412.278.67-4.070.713.89
MDEMLast3.6319.640.004.782.468.74-3.441.134.17
MDEMMin3.5919.640.004.752.468.74-3.441.134.17
MDEMMax3.3019.640.004.442.278.68-4.080.713.89
MDEMMean3.3819.640.004.452.368.43-3.470.993.97
MDEMBlend3.2619.640.004.412.278.67-4.070.713.89

Four categories of the merged DEMs accuracy resulting from the differences between DEMGNSS and the merged DEMs in the case of CDMMs

Merged DEMs1st category high accuracy2nd category medium accuracy3rd category low accuracy4th category very low accuracy
diff. ≤ 1 m [%]1 m < diff. ≤ 3 m [%]3 m< diff. ≤ 5 m [%]diff. > 5 m [%]
MDEMFirst21382318
MDEMLast15402421
MDEMMm15402421
MDEMMax20382319
MDEMMean16412419
MDEMBlend21382318

The statistics of elevations differences between the proposed DEM merging methods and the DEMGNSS

Merged DEMsAbsolute values statisticsBox chart statistics
MAEMax.Min.RMSEMedianWhisker (max.)Whisker (min.)25th percentile75th percentile
a. Zeros border
MDEM0Kriging2.8619.970.004.14-1.715.01-8.63-3.52-0.11
MDEM0IDW2.9822.170.004.30-1.874.91-8.75-3.63-0.22
MDEM0Spline2.8520.170.004.20-1.605.12-8.55-3.43-0.01
MDEMANN2.1922.010.003.710.375.53-4.69-0.861.70
b. H border
MDEMPKriging1.9919.540.003.23-0.014.46-4.79-1.320.99
MDEMPIDW2.1221.930.003.51-0.064.48-5.11-1.510.88
MDEMPSpline2.1520.250.003.48-0.024.89-5.38-1.531.04
MDEMPANN0.854.850.001.160.182.77-2.34-0.430.85

Classification of the elevations differences between DEMGNSS and the merged DEMS in the case of PDMMs

Merged DEMs1st category high accuracy2nd category medium accuracy3rd category low accuracy4th category very low accuracy
diff. ≤ 1 m [%]1 m < diff. ≤ 3 m [%]3 m < diff. ≤ 5 m [%]diff. > 5 m [%]
a. Zeros border
MDEM0KRIGING29361916
MDEM0IDW27372016
MDEM0Spline31361716
MDEMANN41281912
b. H border
MDEMPKRIGING46341010
MDEMPIDW4534912
MDEMPSpline43351012
MDEMPANN7112710

The improvement in elevations, slopes, and aspects by the merged DEMs compared to DEMSRTM based on RMSE

Merged DEMsImprovement [%]
ElevationsSlopesAspects
a. Conventional DEMs merging methods
MDEMFirst7.741.2420.12
MDEMLast0.00-3.1842.96
MDEMMm0.63-3.8738.14
MDEMMax7.11-3.5923.11
MDEMMean6.906.77-23.36
MDEMBlend7.7411.1920.12
b. Proposed DEMs merging methods
MDEM0KRIGING13.3919.6120.12
MDEM0IDW10.0411.4620.17
MDEM0Spline12.131.3820.18
MDEMANN22.3834.6740.28
MDEMPKRIGING32.4329.7042.49
MDEMPIDW26.5727.6241.72
MDEMPSpline27.2018.5141.09
MDEMPANN75.7354.8352.22

Statistics of the slope and aspects differences between DEMGNSS and the merged DEMs in the case of conventional DEMs merging methods

Merged DEMsSlope [%]Aspects [deg]
Min.Max.MeanRMSEMin.Max.MeanRMSE
MDEMFirst-168.9446.223.917.15-352.30360.97219.4289.99
MDEMLast0.0251.635.167.471.00360.97237.5764.26
MDEMMin-155.9250.424.937.52-351.15360.97234.9069.68
MDEMMax-204.9950.374.337.50-352.30360.97221.8586.62
MDEMMean-123.9646.224.156.75-354.10360.7248.57138.97
MDEMBlend-134.8441.073.516.43-352.30360.97219.4289.99

Corners’ coordinates of the study area and the reference point R

PointUTM CoordinatesGeodetic Coordinates
EastingNorthingOrthometric height (H)LongitudeLatitudeEllipsoid height (h)
mmm°°m
P1327256.4483021899.54186.214311515.321271833.41898.977
P2328756.1373023120.616102.13531169.246271913.765114.913
P3330325.3593022440.421127.31731176.662271852.371140.085
P4328521.2543020944.20396.40331161.80227182.955109.153
R329450.6703019874.311109.047311636.137271728.615121.783

Characteristics of the slope and aspects differences between DEMGNSS and the merged DEMs in the case of PDMMs

Merged DEMsSlope [%]Aspects [deg]
Min.Max.MeanRMSEMin.Max.MeanRMSE
a. Zeros border
MDEM0KRIGING-24.7040.513.605.82-352.30360.97219.3589.99
MDEM0IDW-27.2845.063.956.41-352.30360.97219.3989.93
MDEM0Spline-30.5650.984.407.14-352.30360.97219.4089.92
MDEMANN-19.9332.332.924.73-264.23270.73164.6767.28
b. H border
MDEM0KRIGING-21.5135.013.155.09-253.66259.90157.9364.79
MDEM0IDW-22.1436.093.235.24-257.18263.51160.1565.65
MDEM0Spline-25.0241.073.645.90-260.00266.39161.9266.36
MDEMANN-13.7121.992.023.27-211.38216.58131.7353.82

The parameters involved in the ANN algorithm

EpochGoalMax_failMin_failMuLearning rate
1000061e-70.0010.01

Root Mean Square Error (RMSE) of the different GNSS surveying types for TRIMBLE receivers (R8s)

GNSS surveying typeRMSE
HorizontalVertical
Static3 mm + 0.1 ppm3.5 mm + 0.4 ppm
PPK8 mm + 1 ppm15 mm + 1 ppm
RTK8 mm +1 ppm15 mm + 1 ppm

Classification of the difference between DEMGNSS and DEMSRTM into four categories of accuracy

DEM type1st category high accuracy2nd category medium accuracy3rd category low accuracy4th category very low accuracy
diff. ≤ 1 m [%]1 m < diff. ≤ 3 m [%]3 m < diff. ≤ 5 m [%]diff. > 5 m [%]
DEMSRTM14412421

The summary of slope and aspects differences between DEMSRTM and DEMGNSS

DEM typeSlope [%]Aspects [deg]
Min.Max.MeanRMSEMin.Max.MeanRMSE
DEMSRTM-18.4750.470.307.24-351.69359.0732.67112.65
DOI: https://doi.org/10.2478/arsa-2023-0009 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
Language: English
Page range: 122 - 170
Submitted on: Feb 6, 2023
Accepted on: Nov 9, 2023
Published on: Oct 10, 2023
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Mustafa K. Alemam, Bin YONG, Abubakar Sani-Mohammed, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.