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

• Autorzy: Alemam Mustafa K. , Yong Bin , Sani-Mohammed Abubakar

Identyfikatory

DOI

10.2478/arsa-2023-0009

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

digital elevation model; GIS; artificial neural network; interpolation methods; SRTM

• Wydawca: Centrum Badań Kosmicznych PAN ; De Gruyter
• Czasopismo: Artificial Satellites : Journal of Planetary Geodesy
• Rocznik: 2023
• Tom: Vol. 58, No. 3
• Strony: 122--170
• Opis fizyczny: Bibliogr. 74 poz., rys., tab.

Twórcy

autor

Alemam Mustafa K.

• School of Earth Sciences and Engineering, Hohai University, Nanjing, China
• Faculty of Engineering, Assiut University, Assiut, Egypt

autor

Yong Bin

• School of Earth Sciences and Engineering, Hohai University, Nanjing, China
• State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

autor

Sani-Mohammed Abubakar

• Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Bibliografia

• Akturk, E. & A. O. Altunel (2019) Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain. Measurement, 136, 382-386.
• Alemam, M. K., B. Yong & A. S. Mohammed (2022) Integration of Artificial Neural Network and the Optimal GNSS Satellites’ Configuration for Improving GNSS Positioning Techniques (A Case Study in Egypt). Artificial Satellites, 57, 18-46.
• Arun, P. V. (2013) A comparative analysis of different DEM interpolation methods. The Egyptian Journal of Remote Sensing and Space Science, 16, 133-139.
• Badura, J. & B. a. Przybylski (2005) Application of digital elevation models to geological and geomorphological studies-some examples. Przegląd Geologiczny, 53, 977-983.
• Benvenuti, L., C. Kloss & S. Pirker (2016) Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments. Powder technology, 291, 456-465.
• Capolupo, A. (2021) Improving the Accuracy of Global DEM of Differences (DoD) in Google Earth Engine for 3-D Change Detection Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 12332-12347.
• Chen, C. & T. Yue (2010) A method of DEM construction and related error analysis. Computers & Geosciences, 36, 717-725.
• Childs, C. (2004) Interpolating surfaces in ArcGIS spatial analyst. ArcUser, July-September, 3235, 32-35.
• Choussiafis, C., V. Karathanassi & K. Nikolakopoulos. 2012. Mosaic methods for improving the accuracy of interferometric based digital elevation models. In Proccedings of 32nd EARSeL Symposium, 552-560.
• Cook, A. J., T. Murray, A. Luckman, D. G. Vaughan & N. E. Barrand (2012) A new 100-m Digital Elevation Model of the Antarctic Peninsula derived from ASTER Global DEM: methods and accuracy assessment. Earth system science data, 4, 129-142.
• Deilami, B. R., A. Shikhi, M. Liaghat, H. Shahabi, M. Rashid & A. Al-saffar (2012) Improvement the Accuracy of Digital Elevation Model (DEM) with Reduction Pit and Flat Errors (without field work).
• Dekking, F. M., C. Kraaikamp, H. P. Lopuhaä & L. E. Meester. 2005. A Modern Introduction to Probability and Statistics: Understanding why and how. Springer.
• Falorni, G., V. Teles, E. R. Vivoni, R. L. Bras & K. S. Amaratunga (2005) Analysis and characterization of the vertical accuracy of digital elevation models from the Shuttle Radar Topography Mission. Journal of Geophysical Research: Earth Surface, 110.
• Gorsevski, P. V., M. K. Brown, K. Panter, C. M. Onasch, A. Simic & J. Snyder (2016) Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides, 13, 467-484.
• Gruber, A., B. Wessel, M. Huber, M. Breunig & S. Wagenbrenner. 2013. The approach for combining DEM acquisitions for the TanDEM-X DEM mosaic. In 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, 2970-2973. IEEE.
• Gurney, K. 2018. An introduction to neural networks. CRC Press.
• Habib, M., Y. Alzubi, A. Malkawi & M. Awwad (2020) Impact of interpolation techniques on the accuracy of large-scale digital elevation model. Open Geosciences, 12, 190-202.
• Hagan, M., H. Demuth & M. Beale (1996) Neural Network Design (PWS, Boston, MA). Google Scholar Google Scholar Digital Library Digital Library.
• Han, J. & C. Moraga. 1995. The influence of the sigmoid function parameters on the speed of backpropagation learning. In International workshop on artificial neural networks, 195-201. Springer.
• Hawker, L., J. Neal & P. Bates (2019) Accuracy assessment of the TanDEM-X 90 Digital Elevation Model for selected floodplain sites. Remote Sensing of Environment, 232, 111319.
• Haykin, S. 2009. Neural networks and learning machines, 3/E. Pearson Education India.
• Heine, G. W. (1986) A controlled study of some two-dimensional interpolation methods. COGS Computer Contributions, 3, 60-72.
• Houdek, V., O. Verlinden & M. Hajžman (2022) Non-uniform quaternion spline interpolation in vehicle kinematics.
• Huang, Q. & C. Yang (2011) Optimizing grid computing configuration and scheduling for geospatial analysis: An example with interpolating DEM. Computers & Geosciences, 37, 165-176.
• Hugenholtz, C. H., K. Whitehead, O. W. Brown, T. E. Barchyn, B. J. Moorman, A. LeClair, K. Riddell & T. Hamilton (2013) Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model. Geomorphology, 194, 16-24.
• Jain, D., C. Rao, S. R. Kumar & S. Suresh. 2007. Assessment of DEM mosaic accuracy. In SPIE Optics & Photonics 2007: Optical Engineering & Applications Conference.
• Jakovljevic, G., M. Govedarica, F. Alvarez-Taboada & V. Pajic (2019) Accuracy assessment of deep learning based classification of LiDAR and UAV points clouds for DTM creation and flood risk mapping. Geosciences, 9, 323.
• Jana, S. (2011) SELECTION OF APPROPRIATE INTERPOLATION METHODS FOR CREATION DEMs OF VARIOUS TYPES OF RELIEF BY COMPLEX APPROACH TO ASSESSMENT OF DEMs.
• Kawabata, D. & J. Bandibas (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology, 113, 97-109.
• Khemiri, S., I. Chenini, S. Saidi, B. Baamar, A. B. Mammou & F. Zargouni (2013) DEM-Based GIS Algorithms and 3D Geospatial Mapping for Creation of Hydrogeological Models Data in Foussana Basin (Central Tunisia).
• Kim, D. E., S.-Y. Liong, P. Gourbesville, L. Andres & J. Liu (2020) Simple-yet-effective SRTM DEM improvement scheme for dense urban cities using ANN and remote sensing data: application to flood modeling. Water, 12, 816.
• Kulp, S. A. & B. H. Strauss (2018) CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network. Remote sensing of environment, 206, 231-239.
• Leitão, J. P., D. Prodanović & Č. Maksimović (2016) Improving merge methods for grid-based digital elevation models. Computers & geosciences, 88, 115-131.
• Lenda, G., M. Ligas, P. Lewińska & A. Szafarczyk (2016) The use of surface interpolation methods for landslides monitoring. KSCE Journal of Civil Engineering, 20, 188-196.
• Levenberg, K. (1944) A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2, 164-168.
• Lu, G. Y. & D. W. Wong (2008) An adaptive inverse-distance weighting spatial interpolation technique. Computers & geosciences, 34, 1044-1055.
• Marmanis, D., F. Adam, M. Datcu, T. Esch & U. Stilla (2015) Deep neural networks for aboveground detection in very high spatial resolution digital elevation models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 103.
• Marquardt, D. W. (1963) An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11, 431-441.
• Mas, J. F. & J. J. Flores (2008) The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29, 617-663.
• Meng, Q., Z. Liu & B. E. Borders (2013) Assessment of regression kriging for spatial interpolation–comparisons of seven GIS interpolation methods. Cartography and geographic information science, 40, 28-39.
• Mesa-Mingorance, J. L. & F. J. Ariza-López (2020) Accuracy assessment of digital elevation models (DEMs): A critical review of practices of the past three decades. Remote Sensing, 12, 2630.
• Miliaresis, G. C. & C. V. Paraschou (2005) Vertical accuracy of the SRTM DTED level 1 of Crete. International Journal of Applied Earth Observation and Geoinformation, 7, 49-59.
• Mirza, M., G. Dawod & K. Al-Ghamdi (2011) Accuracy and relevance of digital elevation models for geomatics applications-A case study of Makkah municipality, Saudi Arabia. International Journal of Geomatics and Geosciences, 1, 803.
• Mitáš, L. & H. Mitášová (1988) General variational approach to the interpolation problem. Computers & Mathematics with Applications, 16, 983-992.
• Muhadi, N. A., M. S. Mohd Kassim & A. F. Abdullah (2019) Improvement of Digital Elevation Model (DEM) using data fusion technique for oil palm replanting phase. International Journal of Image and Data Fusion, 10, 232-243.
• Mukherjee, S., P. K. Joshi, S. Mukherjee, A. Ghosh, R. Garg & A. Mukhopadhyay (2013) Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21, 205-217.
• Nwilo, P., J. Onyegbula, C. Okolie, O. Daramola, O. Abolaji & I. Arungwa (2022) Influence of land cover, slope, and aspect on the vertical accuracy of SPOT DEM at selected sites in Nigeria. Applied Geomatics, 14, 17-31.
• Oliver, M. A. & R. Webster (1990) Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4, 313-332.
• Reuter, H., P. Strobl & W. Mehl. 2011. How to merge a DEM? In Geomorphometry 2011, 87-90.
• Rishikeshan, C., S. Katiyar & V. V. Mahesh. 2014. Detailed evaluation of DEM interpolation methods in GIS using DGPS data. In 2014 International Conference on Computational Intelligence and Communication Networks, 666-671. IEEE.
• Royle, A. & R. AG (1981) PRACTICAL UNIVERSAL KRIGING AND AUTOMATIC CONTOURNING.
• Sacchi, M. D., T. J. Ulrych & C. J. Walker (1998) Interpolation and extrapolation using a highresolution discrete Fourier transform. IEEE Transactions on Signal Processing, 46, 31-38.
• Sari, I. L., R. Maulana, H. S. Dyatmika, A. Suprijanto, R. Arief & S. Ali (2019) Study of Digital Elevation Model (DEM) Extraction using Stereo Radargrammetry TerraSAR-X in Madiun Area-Elevation Accuracy Improvement.
• Schmidhuber, J. (2015) Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
• Schwendel, A. C., I. C. Fuller & R. G. Death (2012) Assessing DEM interpolation methods for effective representation of upland stream morphology for rapid appraisal of bed stability. River Research and Applications, 28, 567-584.
• Seiffert, U. 2001. Multiple layer perceptron training using genetic algorithms. In ESANN, 159-164. Citeseer.
• Shaikh, M., S. Yadav & V. Manekar (2021) Accuracy assessment of different open-source digital elevation model through morphometric analysis for a semi-arid river basin in the western part of India. Journal of Geovisualization and Spatial Analysis, 5, 1-21.
• Song, H., R. Yuan, Y. Lv, H. Liu & Y. Li (2022) Cubic spline interpolation-based refined composite multiscale dispersion entropy and its application to bearing fault identification. Structural Health Monitoring, 14759217221134050.
• Suwandana, E., K. Kawamura, Y. Sakuno, E. Kustiyanto & B. Raharjo (2012) Evaluation of ASTER GDEM2 in comparison with GDEM1, SRTM DEM and topographic-map-derived DEM using inundation area analysis and RTK-dGPS data. Remote Sensing, 4, 2419-2431.
• Thomas, K. 2010. Introductory business statistics.
• Varga, M. & T. Bašić (2015) Accuracy validation and comparison of global digital elevation models over Croatia. International journal of remote sensing, 36, 170-189.
• Warriner, T. 2005. Generating a new high resolution DTM product from various data sources. In Proceedings of the 50th Photogrammetric Week. Citeseer.
• Wendi, D., S. Y. Liong, Y. Sun & C. D. Doan (2016) An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network. Journal of Advances in Modeling Earth Systems, 8, 691-702.
• Wickham, H. & L. Stryjewski (2011) 40 years of boxplots. Am. Statistician, 2011.
• Wilson, J. P. (2012) Digital terrain modeling. Geomorphology, 137, 107-121.
• Xiong, L., G. Wang & P. Wessel (2017) Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas. Computers & Geosciences, 100, 125-134.
• Xu, J. & B. Zhou (2016) A Method of Recovering Distorted DEM of Regular Terrain.
• Yamazaki, D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O'Loughlin, J. C. Neal, C. C. Sampson, S. Kanae & P. D. Bates (2017) A high‐accuracy map of global terrain elevations. Geophysical Research Letters, 44, 5844-5853.
• Yang, C.-S., S.-P. Kao, F.-B. Lee & P.-S. Hung. 2004. Twelve different interpolation methods: A case study of Surfer 8.0. In Proceedings of the XXth ISPRS congress, 778-785.
• Yang, L., X. Meng & X. Zhang (2011) SRTM DEM and its application advances. International Journal of Remote Sensing, 32, 3875-3896.
• Yap, L., L. H. Kandé, R. Nouayou, J. Kamguia, N. A. Ngouh & M. B. Makuate (2019) Vertical accuracy evaluation of freely available latest high-resolution (30 m) global digital elevation models over Cameroon (Central Africa) with GPS/leveling ground control points. International Journal of Digital Earth, 12, 500-524.
• Ye, F., C. Wheeler, B. Chen, J. Hu, K. Chen & W. Chen (2019) Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network. Advanced Powder Technology, 30, 292-301.
• Yue, T.-X., Z.-P. Du, D.-J. Song & Y. Gong (2007) A new method of surface modeling and its application to DEM construction. Geomorphology, 91, 161-172.
• Zhou, H., J. Zhang, L. Gong & X. Shang (2012) Comparison and validation of different DEM data derived from InSAR. Procedia Environmental Sciences, 12, 590-597.