Cited by 139

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications

Okolie, Chukwuma J., Smit, Julian L.·2022·ISPRS Journal of Photogrammetry and Remote Sensing

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.isprsjprs.2022.03.016

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation.

    “DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation.”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [8]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 175

  1. Aghajani, 2016, A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm, Energy Convers. Manage., 121, 232, 10.1016/j.enconman.2016.05.024 · DOI
  2. Agrawal, 2018, Accuracy Assessment of Digital Elevation Model Generated by SAR Stereoscopic Technique Using COSMO-Skymed Data, J. Indian Soc. Remote Sens., 46, 1739, 10.1007/s12524-018-0835-6 · DOI
  3. Ajibola, 2019, Fusion of UAV-based DEMs for vertical component accuracy improvement, Measurement: J. Int. Measurement Confederation, 147, 106795, 10.1016/j.measurement.2019.07.023 · DOI
  4. Albino, 2020, Using TanDEM-X to measure pyroclastic flow source location, thickness and volume: Application to the 3rd June 2018 eruption of Fuego volcano, Guatemala, J. Volcanol. Geoth. Res., 406, 107063, 10.1016/j.jvolgeores.2020.107063 · DOI
  5. Arief, 2020, A fusion of digital elevation model based on interferometry SAR technique from ascending and descending path in urban area, IOP Conf. Ser.: Earth Environ. Sci., 500, 012035, 10.1088/1755-1315/500/1/012035 · DOI
  6. Avtar, 2015, Evaluation of DEM generation based on Interferometric SAR using TanDEM-X data in Tokyo, Phys. Chem. Earth., 83, 166, 10.1016/j.pce.2015.07.007 · DOI
  7. Bagheri, 2017, Uncertainty assessment and weight map generation for efficient fusion of Tandem-x and Cartosat-1 DEMS, Int. Arch. Photogrammetry Remote Sens. Spatial Information Sci. - ISPRS Arch., 42, 433
  8. Bagheri, 2017, Fusion of TanDEM-X and Cartosat-1 DEMS using TV-norm regularization and ANN-predicted weights, IEEE Int. Geosci. Remote Sens. Symposium (IGARSS), 2017, 3369
  9. Bagheri, 2019, Fusion of multi-sensor-derived heights and OSM-derived building footprints for urban 3D reconstruction, ISPRS Int. J. Geo-Inf., 8, 1, 10.3390/ijgi8040193 · DOI
  10. Bagheri, 2018, Fusion of TanDEM-X and Cartosat-1 elevation data supported by neural network-predicted weight maps, ISPRS J. Photogramm. Remote Sens., 144, 285, 10.1016/j.isprsjprs.2018.07.007 · DOI
  11. Bagheri, 2018, Fusion of Urban TanDEM-X Raw DEMs Using Variational Models, IEEE J. Selected Topics Appl. Earth Observations Remote Sens., 11, 4761, 10.1109/JSTARS.2018.2878608 · DOI
  12. Ballester, 2006, A Variational Model for P+XS Image Fusion, Int. J. Comput Vis., 69, 43, 10.1007/s11263-006-6852-x · DOI
  13. Bastin, 2002, Visualizing uncertainty in multi-spectral remotely sensed imagery, Comput. Geosci., 28, 337, 10.1016/S0098-3004(01)00051-6 · DOI
  14. Belayneh, 2016, Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction, Atmos. Res., 172–173, 37, 10.1016/j.atmosres.2015.12.017 · DOI
  15. Bredies, 2010, Total Generalized Variation, SIAM J. Imaging Sci., 3, 492, 10.1137/090769521 · DOI
  16. Buckley, 2004, Integration, validation and point spacing optimisation of digital elevation models, Photogrammetric Record, 19, 277, 10.1111/j.0031-868X.2004.00287.x · DOI
  17. Cenci, 2021, Describing the Quality Assessment Workflow Designed for DEM Products Distributed Via the Copernicus Programme. Case Study: The Absolute Vertical Accuracy of the Copernicus DEM Dataset in Spain, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6143, 10.1109/IGARSS47720.2021.9554393 · DOI
  18. Chaabane, 2008, Interferometric data fusion for topographic profile reconstruction, Int. Geosci. Remote Sens. Symposium (IGARSS), 4, 798
  19. Chaplot, 2006, Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density, Geomorphology, 77, 126, 10.1016/j.geomorph.2005.12.010 · DOI
  20. Chen, 2010, A method of DEM construction and related error analysis, Comput. Geosci., 36, 717, 10.1016/j.cageo.2009.12.001 · DOI
  21. Chen, 2016, Information from imagery: ISPRS scientific vision and research agenda, ISPRS J. Photogramm. Remote Sens., 115, 3, 10.1016/j.isprsjprs.2015.09.008 · DOI
  22. Chen, K., 2013. Introduction to variational image-processing models and applications. 90(1), 1–8. 10.1080/00207160.2012.757073. · DOI · Paper page
  23. Chen, 2018, Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm, Appl. Sci., 8, 2206, 10.3390/app8112206 · DOI
  24. Chou, 1994, Multiscale recursive estimation, data fusion, and regularization, IEEE Trans. Autom. Control, 39, 464, 10.1109/9.280746 · DOI
  25. Chu, 2014, Identifying LiDAR sample uncertainty on terrain features from DEM simulation, Geomorphology, 204, 325, 10.1016/j.geomorph.2013.08.016 · DOI
  26. Chu, 2017, RADARSAT-2-based digital elevation models derived from InSAR for high latitudes of northern Canada, J. Appl. Remote Sens., 11, 1, 10.1117/1.JRS.11.035013 · DOI
  27. Clint Slatton, 2001, Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights, IEEE Trans. Geosci. Remote Sens., 39, 2470, 10.1109/36.964984 · DOI
  28. Crippen, 2016, NASADEM global elevation model: Methods and progress., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41, 125
  29. d'Ozouville, 2008, DEM generation using ASAR (ENVISAT) for addressing the lack of freshwater ecosystems management, Santa Cruz Island, Galapagos, Remote Sens. Environ., 112, 4131, 10.1016/j.rse.2008.02.017 · DOI
  30. Danielson, J.J., Gesch, D. B. (2011). Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). U.S. Geological Survey Open-File Report 2011-1073, 2010, 26. http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/GMTED2010. · DOI
  31. Deng, 2019, High-resolution DEM generation from spaceborne and terrestrial remote sensing data for improved volcano hazard assessment — A case study at Nevado del Ruiz, Colombia, Remote Sens. Environ., 233, 111348, 10.1016/j.rse.2019.111348 · DOI
  32. Deng, 2011, DEM Fusion and Its Application in Mapping Topography of Complex Areas, Int. Symposium Image Data Fusion, 2011, 1
  33. Deo, 2015, Framework for Fusion of Ascending and Descending Pass TanDEM-X Raw DEMs, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 3347, 10.1109/JSTARS.2015.2431433 · DOI
  34. Dong, 2021, Cascaded multi-baseline interferometry with bistatic TerraSAR-X/TanDEM-X observations for DEM generation, ISPRS J. Photogramm. Remote Sens., 171, 224, 10.1016/j.isprsjprs.2020.11.012 · DOI
  35. Dong, 2018, Fusion of multi-baseline and multi-orbit InSAR DEMs with terrain feature-guided filter, Remote Sens., 10, 1, 10.3390/rs10101511 · DOI
  36. Du, 2019, A Method for Surface Water Body Detection and DEM Generation with Multigeometry TanDEM-X Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 12, 151, 10.1109/JSTARS.2018.2883772 · DOI
  37. Dubayah, 2020, The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography, Sci. Remote Sens., 1, 100002, 10.1016/j.srs.2020.100002 · DOI · Paper page
  38. E.Fuss, C., 2013. Digital Elevation Model Generation and Fusion. Master Thesis, 159. https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/7571/Fuss_Colleen_201309_Msc.pdf?sequence=3.
  39. Elkhrachy, 2018, Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia, Ain Shams Eng. J., 9, 1807, 10.1016/j.asej.2017.01.007 · DOI
  40. Equis, 2009, The empirical mode decomposition: a must-have tool in speckle interferometry?, Optics Express, 17, 611, 10.1364/OE.17.000611 · DOI
  41. Falorni, 2005, Analysis and characterization of the vertical accuracy of digital elevation models from the Shuttle Radar Topography Mission, J. Geophys. Res. Earth Surf., 110, 1
  42. Ferretti, 1999, Multibaseline insar dem reconstruction: the wavelet approach, IEEE Trans. Geosci. Remote Sens., 37, 705, 10.1109/36.752187 · DOI
  43. Fisher, 2006, Causes and consequences of error in digital elevation models, Prog. Phys. Geogr., 30, 467, 10.1191/0309133306pp492ra · DOI · Paper page
  44. Fu, 2016, Statistic tests aided multi-source DEM fusion, Int. Arch. Photogrammetry, Remote Sens. Spatial Information Sciences - ISPRS Arch., 41, 227
  45. Fuss, 2016, DEM Fusion using a modified k-means clustering algorithm, Int. J. Digital Earth, 9, 1242, 10.1080/17538947.2016.1208685 · DOI
  46. Gallant, 2012, Removal of tree offsets from SRTM and other digital surface models, ISPRS – Int Arch. Photogrammetry, Remote Sens. Spatial Information Sci., 275
  47. Gamba, 2003, Comparison and fusion of LIDAR and InSAR digital elevation models over urban areas, Int. J. Remote Sens., 24, 4289, 10.1080/0143116031000096005 · DOI
  48. Gao, 2010, Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: A comparison, IEEE Signal Process Lett., 17, 237, 10.1109/LSP.2009.2037773 · DOI
  49. Gavriil, 2019, Void Filling of Digital Elevation Models with Deep Generative Models, IEEE Geosci. Remote Sens. Lett., 16, 1645, 10.1109/LGRS.2019.2902222 · DOI
  50. Gelautz, 2003, Radar stereo- and interferometry-derived digital elevation models: Comparison and combination using Radarsat and ERS-2 imagery, Int. J. Remote Sens., 24, 5243, 10.1080/0143116031000115139 · DOI
  51. Gesch, 1999, New land surface digital elevation model covers the earth, Eos, 80, 69, 10.1029/99EO00050 · DOI
  52. Ghassemian, 2016, A review of remote sensing image fusion methods, Information Fusion, 32, 75, 10.1016/j.inffus.2016.03.003 · DOI
  53. Grohman, 2006, Filling SRTM voids: The delta surface fill method, Photogrammetr. Eng. Remote Sens., 213
  54. Gruber, 2016, The TanDEM-X DEM Mosaicking: Fusion of Multiple Acquisitions Using InSAR Quality Parameters, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 1047, 10.1109/JSTARS.2015.2421879 · DOI
  55. Gruen, 2005, Least squares 3D surface and curve matching, ISPRS J. Photogramm. Remote Sens., 59, 151, 10.1016/j.isprsjprs.2005.02.006 · DOI · Paper page
  56. Guan, 2020, Fusion of public DEMs based on sparse representation and adaptive regularization variation model, ISPRS J. Photogramm. Remote Sens., 169, 125, 10.1016/j.isprsjprs.2020.09.005 · DOI
  57. Guan, Ling, Gao, L., El Din Elmadany, N., & Liang, C. (2018). Statistical Machine Learning vs Deep Learning in Information Fusion: Competition or Collaboration? In: Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018, 251–256. 10.1109/MIPR.2018.00059. · DOI
  58. Guardiano, 1993, Multivariate Geostatistics: Beyond Bivariate Moments.
  59. Hawker, 2022, A 30 m global map of elevation with forests and buildings removed, Environ. Res. Lett., 17, 10.1088/1748-9326/ac4d4f · DOI · Paper page
  60. Hepburn, 2019, Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline, Geosci. Instrum. Methods Data Syst., 8, 293, 10.5194/gi-8-293-2019 · DOI
  61. Hoja, D., d’Angelo, P., 2009. Analysis of DEM combination methods using high resolution optical stereo imagery and interferometric SAR data. https://semanticscholar.org/paper/95922900bdc8cb768b22fd63333e12d8bd2a8592.
  62. Hoja, 2006, Comparison of DEM generation and combination methods using high resolution optical stereo imagery and interferometric sar data, Revue Francaise de Photogrammetrie et de Teledetection, 184, 89
  63. Hu, 2009, Multifractal analysis of sunspot time series: the effects of the 11-year cycle and Fourier truncation, JSMTE, 2009, 02066
  64. Huang, 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903, 10.1098/rspa.1998.0193 · DOI
  65. Humphreys, 2020, Advancing Fusion with Machine Learning Research Needs Workshop Report, J Fusion Energ, 39, 123, 10.1007/s10894-020-00258-1 · DOI · Paper page
  66. Hutchinson, 1989, A new procedure for gridding elevation and stream line data with automatic removal of spurious pits, J. Hydrol., 106, 211, 10.1016/0022-1694(89)90073-5 · DOI
  67. Hutchinson, M., Gallant, J., 2000. Digital elevation models and representation of terrain shape. https://semanticscholar.org/paper/aaaf34b076e03643f2b0d4bd9fe2639389912213.
  68. IEEE GRSS Strategic Action Plan, 2020. IEEE GRSS Strategic Plan November 2020 DRAFT. https://4d8xuv2577wggnmowfwzehpo-wpengine.netdna-ssl.com/wp-content/uploads/2021/02/20201107_GRSS_StrategicPlan_DRAFT.pdf.
  69. Iheaturu, 2020, AN assessment of the accuracy of structure-from-motion (SFM) photogrammetry for 3D terrain mapping, Geomatics Landmanagement Landscape, 2, 65
  70. Jain, 2014, Evaluation of time series TanDEM-X Digital Elevation Models, Int. Arch. Photogrammetry, Remote Sens. Spatial Information Sci. - ISPRS Arch., 40, 437
  71. Jiang, 2014, Fusion of high-resolution DEMs derived from COSMO-SkyMed and TerraSAR-X InSAR datasets, J. Geod., 88, 587, 10.1007/s00190-014-0708-x · DOI
  72. Joshi, 2005, A learning-based method for image super-resolution from zoomed observations, IEEE Trans. Syst. Man Cybern. B Cybern., 35, 527, 10.1109/TSMCB.2005.846647 · DOI
  73. Kääb, 2005, Combination of SRTM3 and repeat ASTER data for deriving alpine glacier flow velocities in the Bhutan Himalaya, Remote Sens. Environ., 94, 463, 10.1016/j.rse.2004.11.003 · DOI
  74. Kalman, 1960, A New Approach to Linear Filtering and Prediction Problems, J. Basic Eng., 82, 35, 10.1115/1.3662552 · DOI · Paper page
  75. Karakasis, E.G., Bampis, L., Amanatiadis, A., Gasteratos, A., Tsalides, P., 2014. Digital elevation model fusion using spectral methods. In: IST 2014 - 2014 IEEE International Conference on Imaging Systems and Techniques, Proceedings, 3, 340–345. 10.1109/IST.2014.6958501. · DOI
  76. Karkee, 2008, Improving quality of public domain digital elevation models through data fusion, Biosyst. Eng., 101, 293, 10.1016/j.biosystemseng.2008.09.010 · DOI
  77. Kaur, 2021, Image Fusion Techniques: A Survey, Arch. Comput. Methods Eng., 28, 4425, 10.1007/s11831-021-09540-7 · DOI
  78. Kim, 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, 10.3390/w12030816 · DOI
  79. Knöpfle, W., Strunz, G., Roth, A., 1998. Mosaicking of Digital Elevation Models derived by SAR Interferometry. https://semanticscholar.org/paper/fbeb44e27d73246575c220ff6ecb4fa8c2f85057.
  80. Kraus, 1998, Determination of terrain models in wooded areas with airborne laser scanner data, ISPRS J. Photogramm. Remote Sens., 53, 193, 10.1016/S0924-2716(98)00009-4 · DOI
  81. Kulkarni, 2020, Pixel level fusion techniques for SAR and optical images : A review, Information Fusion, 59, 13, 10.1016/j.inffus.2020.01.003 · DOI
  82. Kulp, 2018, CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network, Remote Sens. Environ., 206, 231, 10.1016/j.rse.2017.12.026 · DOI
  83. Kuschk, 2013, Fusion of Multi-Resolution Digital Surface Models, ISPRS – Int. Arch. Photogrammetry, Remote Sens. Spatial Inf. Sci., XL-1/W3(October), 247
  84. Kuschk, 2017, Spatially Regularized Fusion of Multiresolution Digital Surface Models, IEEE Trans. Geosci. Remote Sens., 55, 1477, 10.1109/TGRS.2016.2625040 · DOI
  85. Lary, 2018, Machine Learning Applications for Earth Observation, Earth Observation Open Science and Innovation, 165–218, 10.1007/978-3-319-65633-5_8 · DOI
  86. Leclerc, Y., Luong, Q.T., and Fua, P., 1998a. A framework for detecting changes in terrain. In ARPA Image Understanding Workshop, Monterey, CA,1998.
  87. Leclerc, 1998, Self-consistency: a novel approach to characterizing the accuracy and reliability of point-correspondence algorithms, DARPA Image Understanding Workshop
  88. Lei, 2021, Automated estimation of forest height and underlying topography over a Brazilian tropical forest with single-baseline single-polarization TanDEM-X SAR interferometry, Remote Sens. Environ., 252, 112132, 10.1016/j.rse.2020.112132 · DOI
  89. Leitão, 2018, Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment, J. Hydrol., 561, 651, 10.1016/j.jhydrol.2018.04.043 · DOI
  90. Li, 1992, Variation of the accuracy of digital terrain models with sampling interval, Photogram. Rec., 14, 113, 10.1111/j.1477-9730.1992.tb00211.x · DOI
  91. Liu, C., Ding, W., 2019. Variational model for infrared and visible light image fusion with saliency preservation. 28(2), 023023. 10.1117/1.JEI.28.2.023023. · DOI
  92. Liu, H., Qian, Y., Zhong, X., Chen, L., Yang, G., 2021. Research on super-resolution reconstruction of remote sensing images: a comprehensive review. 60(10), 100901. 10.1117/1.OE.60.10.100901. · DOI
  93. Liu, 2018, Deep learning for pixel-level image fusion: Recent advances and future prospects, Information Fusion, 42, 158, 10.1016/j.inffus.2017.10.007 · DOI
  94. Mallat, S., 2009. Chapter 12: Sparsity in Redundant Dictionaries. A Wavelet Tour of Signal Processing, Third ed., The Sparse Way, 611–698. · DOI
  95. Mankoff, 2013, The Kinect: a low-cost, high-resolution, short-range 3D camera, Earth Surf. Proc. Land., 38, 926, 10.1002/esp.3332 · DOI
  96. Mascarenhas, N., Banon, G., Candeias, A.L.B., 1992. Image Data Fusion under A Bayesian Approach. In: [Proceedings] IGARSS ’92 International Geoscience and Remote Sensing Symposium, 1, 675–677. 10.1109/IGARSS.1992.576802. · DOI
  97. Maxwell, 2018, Implementation of machine-learning classification in remote sensing: An applied review, Int. J. Remote Sens., 39, 2784, 10.1080/01431161.2018.1433343 · DOI
  98. Miliaresis, 1999, Segmentation of physiographic features from the global digital elevation model/GTOPO30, Comput. Geosci., 25, 715, 10.1016/S0098-3004(99)00025-4 · DOI
  99. Mouratidis, 2019, European Digital Elevation Model Validation against Extensive Global Navigation Satellite Systems Data and Comparison with SRTM DEM and ASTER GDEM in Central Macedonia (Greece), ISPRS Int. J. Geo-Information, 8, 108, 10.3390/ijgi8030108 · DOI
  100. Nunes, 2003, Image analysis by bidimensional empirical mode decomposition, Image Vis. Comput., 21, 1019, 10.1016/S0262-8856(03)00094-5 · DOI
  101. Nwilo, P.C., Ayodele, E.G., and Okolie, C.J., 2017. Determination of the Impacts of Landscape Offsets on the 30-metre SRTM DEM through a comparative analysis with Bare-Earth Elevations. FIG Peer Review Journal. 21 pps. FIG. http://fig.net/resources/publications/prj/showpeerreviewpaper.asp?pubid=8560.
  102. Olufunmilayo Gbopa, 2021, Unmanned Aerial Vehicles for Three-dimensional Mapping and Change Detection Analysis, Geomatics Environ. Eng., 15, 41, 10.7494/geom.2021.15.1.41 · DOI
  103. Onwudinjo, 2019, Evaluating The performance of multi-rotor Unmanned Aerial Vehicle –Structure From Motion (UAV-SfM) imagery in assessing simple and complex forest structures: comparison to airborne and Terrestrial Laser Scanning., 6th UCT EBE Research Expo
  104. Onwudinjo, 2022, Estimating the Performance of Multi-Rotor Unmanned Aerial Vehicle Structure-from-Motion (UAVSfM) Imagery in Assessing Homogeneous and Heterogeneous Forest Structures: A Comparison to Airborne and Terrestrial Laser Scanning, South Afr. J. Geomat., 11, 65
  105. Page, 2021, PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews, BMJ (Clin. Res., Ed.), 372
  106. Papasaika-Hanusch, 2012, Fusion of digital elevation models
  107. Papasaika, 2008, A framework for the fusion of digital elevation models, Int. Arch. Photogrammetry, Remote Sens. Spatial Information Sci., XXXVII, 811
  108. Park, 2003, Super-resolution image reconstruction: a technical overview, IEEE Signal Process Mag., 20, 21, 10.1109/MSP.2003.1203207 · DOI
  109. Perko, 2016, Globally optimal robust DSM fusion, Eur. J. Remote Sens., 49, 489, 10.5721/EuJRS20164926 · DOI
  110. Petrasova, 2017, Fusion of high-resolution DEMs for water flow modeling, Open Geospatial Data, Software Standards, 2, 10.1186/s40965-017-0019-2 · DOI
  111. Pham, 2018, A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data, Remote Sens. Environ., 210, 229, 10.1016/j.rse.2018.03.026 · DOI
  112. Pipaud, 2015, Evaluation of TanDEM-X elevation data for geomorphological mapping and interpretation in high mountain environments — A case study from SE Tibet, China, Geomorphology, 246, 232, 10.1016/j.geomorph.2015.06.025 · DOI
  113. Pock, 2011, TGV-Fusion.
  114. Podobnikar, 2005, Production of integrated digital terrain model from multiple datasets of different quality, Int. J. Geographical Information Sci., 19, 69, 10.1080/13658810412331280130 · DOI
  115. Pohl, 1998, Multisensor image fusion in remote sensing: Concepts, methods and applications, Int. J. Remote Sens., 19, 823, 10.1080/014311698215748 · DOI
  116. Pohl, 2016, Preprocessing, Remote Sensing Image Fusion, 71–92
  117. Polidori, 2020, Methods : A Critical Review, Remote Sens., 12, 3522, 10.3390/rs12213522 · DOI
  118. Qi, 2016, Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping, Remote Sens. Environ., 187, 253, 10.1016/j.rse.2016.10.018 · DOI
  119. Qiu, 2019, Void Filling of Digital Elevation Models with a Terrain Texture Learning Model Based on Generative Adversarial Networks, Remote. Sens., 11, 2829, 10.3390/rs11232829 · DOI
  120. Rasera, 2019, Downscaling Images with Trends Using Multiple-Point Statistics Simulation: An Application to Digital Elevation Models, Math. Geosci., 52, 145, 10.1007/s11004-019-09818-4 · DOI
  121. Rasera, 2015, A conflict-free, path-level parallelization approach for sequential simulation algorithms, Comput. Geosci., 80, 49, 10.1016/j.cageo.2015.03.016 · DOI
  122. Ravanbakhsh, 2013, A comparative study of DEM registration approaches, J. Spatial Sci., 58, 79, 10.1080/14498596.2012.759091 · DOI
  123. Reuter, 2007, An evaluation of void-filling interpolation methods for SRTM data, Int. J. Geographical Information Sci., 21, 983, 10.1080/13658810601169899 · DOI
  124. Rizzoli, 2017, Generation and performance assessment of the global TanDEM-X digital elevation model, ISPRS J. Photogramm. Remote Sens., 132, 119, 10.1016/j.isprsjprs.2017.08.008 · DOI
  125. Robinson, 2014, EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data, ISPRS J. Photogramm. Remote Sens., 87, 57, 10.1016/j.isprsjprs.2013.11.002 · DOI
  126. Rossi, C., Eineder, M., Fritz, T., d’Angelo, P., & Reinartz, P., 2013. Quality assessment of TanDEM-X Raw DEMs oriented to a fusion with CartoSAT-1 DEMs. https://www.semanticscholar.org/paper/8dced06056c60e8345ab919f872376cdab204ac5.
  127. Rossi, 2016, Temporal monitoring of subglacial volcanoes with TanDEM-X — Application to the 2014–2015 eruption within the Bárðarbunga volcanic system, Iceland, Remote Sens. Environ., 181, 186, 10.1016/j.rse.2016.04.003 · DOI
  128. Roth, A., Knöpfle, W., Strunz, G., Lehner, M., Reinartz, P., 2002. Towards a global elevation product: combination of multi-source digital elevation models. https://semanticscholar.org/paper/d5c6191ec65587f711b945f24333d7c3d838b2e3.
  129. Roujean, 2018, Inversion of Lumped Parameters Using BRDF Kernels, Comprehensive Remote Sens., 1–9, 23, 10.1016/B978-0-12-409548-9.10346-X · DOI
  130. Rudin, 1992, Nonlinear total variation based noise removal algorithms, Physica D, 60, 259, 10.1016/0167-2789(92)90242-F · DOI
  131. Sadeq, 2016, Merging digital surface models implementing Bayesian approaches, Int. Arch. Photogrammetry, Remote Sens. Spatial Information Sci. - ISPRS Arch., 41, 711
  132. Salentinig, 2015, Combining SAR-Based and Multispectral-Based Extractions to Map Urban Areas at Multiple Spatial Resolutions, IEEE Geosci. Remote Sens. Mag., 3, 100, 10.1109/MGRS.2015.2430874 · DOI
  133. Samson, 2000, A variational model for image classification and restoration, IEEE Trans. Pattern Anal. Mach. Intell., 22, 460, 10.1109/34.857003 · DOI
  134. Schiewe, 2003, Integration of multi-sensor data for landscape modeling using a region-based approach, ISPRS J. Photogramm. Remote Sens., 57, 371, 10.1016/S0924-2716(02)00165-X · DOI
  135. Schindler, 2011, Improving Wide-Area DEMs Through Data Fusion – Chances and Limits, Photogrammetric Week, ’11, 159
  136. Schmitt, 2016, Data Fusion and Remote Sensing: An ever-growing relationship, IEEE Geosci. Remote Sens. Mag., 4, 6, 10.1109/MGRS.2016.2561021 · DOI
  137. Schultz, H., Hanson, A.R., Dong-Min, W., Riseman, E. M., Stolle, F.R., Zhu, Z., 2002. A self-consistency technique for fusing 3D information. In: Proceedings of the 5th International Conference on Information Fusion, FUSION 2002, 2, 1106–1112. 10.1109/ICIF.2002.1020936. · DOI
  138. Schultz, H., Riseman, E.M., Stolle, F.R., Woo, D.M., 1999. Error detection and DEM fusion using self-consistency. In: Proceedings of the IEEE International Conference on Computer Vision, 2(c), 1174–1181. 10.1109/iccv.1999.790413. · DOI
  139. Schumann, 2018, The Need for a High-Accuracy, Open-Access Global DEM, Front. Earth Sci., 6, 225, 10.3389/feart.2018.00225 · DOI
  140. Schumann, 2016, High-accuracy elevation data at large scales from airborne single-pass SAR interferometry, Front. Earth Sci., 3, 88, 10.3389/feart.2015.00088 · DOI
  141. Shi, 2003, A Study on Bayes Feature Fusion for Image Classification, IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition Workshops, 8
  142. Slatton, K.C., Crawford, M., Teng, L., 2002. Multiscale fusion of INSAR data for improved topographic mapping. International Geoscience and Remote Sensing Symposium (IGARSS), 1(C), 69–71. 10.1109/igarss.2002.1024944. · DOI
  143. Simard, 2016, Validation of the new SRTM digital elevation model (NASADEM) with ICESAT/GLAS over the united states, International Geoscience and Remote Sensing Symposium (IGARSS), 3227
  144. Smith, 2001, Mars Orbiter Laser Altimeter: Experiment summary after the first year of global mapping of Mars, J. Geophys. Res. Planets, 106, 23689, 10.1029/2000JE001364 · DOI
  145. Song, 2015, Improving the spatial resolution of landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution, IEEE Trans. Geosci. Remote Sens., 53, 1195, 10.1109/TGRS.2014.2335818 · DOI
  146. Tang, 2015, Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation, ISPRS J. Photogramm. Remote Sens., 101, 174, 10.1016/j.isprsjprs.2014.12.016 · DOI
  147. Tang, 2015, Digital Elevation Data Fusion Using Multiple-Point Geostatistical Simulation, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 4922, 10.1109/JSTARS.2015.2438299 · DOI
  148. Tian, 2018, Improving the accuracy of open source digital elevation models with multi-scale fusion and a slope position-based linear regression method, Remote Sens., 10, 1861, 10.3390/rs10121861 · DOI
  149. Tran, 2014, A geomorphology-based approach for digital elevation model fusion - Case study in Danang city, Vietnam, Earth Surface Dyn., 2, 403, 10.5194/esurf-2-403-2014 · DOI
  150. Treuhaft, 2000, Vertical structure of vegetated land surfaces from interferometric and polarimetric radar, Radio Sci., 35, 141, 10.1029/1999RS900108 · DOI
  151. Tridon, 2013, TanDEM-X: DEM acquisition in the third year era, Int. J. Space Sci. Eng., 1, 367, 10.1504/IJSPACESE.2013.059270 · DOI
  152. Tung, 2011, Detecting chaos in heavy-noise environments, Phys. Rev. E, 83, 10.1103/PhysRevE.83.046210 · DOI
  153. UK Parliament POST, 2020, Remote sensing and machine learning, UK Parliament POSTNOTE No., 628, 1
  154. van Genderen, 1994, Image fusion : issues, techniques and, applications. 10.2/JQUERY.MIN.JS
  155. Van Niel, 2008, The impact of misregistration on SRTM and DEM image differences, Remote Sens. Environ., 112, 2430, 10.1016/j.rse.2007.11.003 · DOI
  156. Vanthof, 2019, Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations, Remote Sens. Environ., 235, 111437, 10.1016/j.rse.2019.111437 · DOI
  157. Vosselman, G., Vosselman, G., Maas, H., 2001. Adjustment and filtering of raw laser altimetry data. In: Proceedings of Oeepe Workshop on Airborne Laserscanning and Interferometric SAR For Detailed Digital Terrain Models. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.461.3115.
  158. Wang, 2020, Variational model for simultaneously image denoising and contrast enhancement, Opt. Express, 28, 18751, 10.1364/OE.28.018751 · DOI
  159. Wechsler, 2007, Uncertainties associated with digital elevation models for hydrologic applications: a review, Hydrol. Earth Syst. Sci., 11, 1481, 10.5194/hess-11-1481-2007 · DOI · Paper page
  160. Wendi, 2016, An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network, J. Adv. Modeling Earth Syst., 8, 691, 10.1002/2015MS000536 · DOI
  161. Williams, B. M., Spencer, J. A., Chen, K., Zheng, Y., Harding, S., 2016. An effective variational model for simultaneous reconstruction and segmentation of blurred images. 10(4), 244–264. 10.1177/1748301816660406. · DOI
  162. Yamazaki, 2017, A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844, 10.1002/2017GL072874 · DOI · Paper page
  163. Yang, 2010, Image super-resolution via sparse representation, IEEE Trans. Image Process., 19, 2861, 10.1109/TIP.2010.2050625 · DOI
  164. Yang, 2011, SRTM DEM and its application advances, 32, 3875
  165. Yue, 2016, Image super-resolution: The techniques, applications, and future, Sig. Process., 128, 389, 10.1016/j.sigpro.2016.05.002 · DOI · Paper page
  166. Yue, 2015, Fusion of multi-scale DEMs using a regularized super-resolution method, Int. J. Geographical Information Sci., 29, 2095, 10.1080/13658816.2015.1063639 · DOI
  167. Zakeri, 2021, A review of geostatistical simulation models applied to satellite remote sensing: Methods and applications, Remote Sens. Environ., 259, 112381, 10.1016/j.rse.2021.112381 · DOI
  168. Zhang, 2010, Multi-source remote sensing data fusion: Status and trends, Int. J. Image Data Fusion, 1, 5, 10.1080/19479830903561035 · DOI
  169. Zhang, 2016, Bathymetric survey of water reservoirs in north-eastern Brazil based on TanDEM-X satellite data, Sci. Total Environ., 571, 575, 10.1016/j.scitotenv.2016.07.024 · DOI
  170. Zhou, Z., Jiang, L., Wang, J., Zhang, P., Yang, P., 2011. Image fusion by combining SWT and variational model. In: Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011, 4, 1907–1910. 10.1109/CISP.2011.6100633. · DOI
  171. Arungwa, 2018, Validation of global digital elevation models in Lagos State, Nigeria, Nigerian J. Environ. Sci. Technol., 2, 78, 10.36263/nijest.2018.01.0058 · DOI
  172. Nwilo, 2020, Vertical accuracy assessment of 20-metre SPOT DEM using ground control points from Lagos and FCT, Nigeria, J. Eng. Res., 25, 153, 10.52968/72013764 · DOI
  173. Nwilo, 2022, Influence of land cover, slope, and aspect on the vertical accuracy of SPOT DEM at selected sites in Nigeria, Appl. Geomat., 14, 17, 10.1007/s12518-021-00404-0 · DOI
  174. Olusina, 2018, Visualisation of uncertainty in 30m resolution Global Digital Elevation Models: SRTM v3. 0 and ASTER v2, Nigerian J. Technol. Dev., 15, 77, 10.4314/njtd.v15i3.2 · DOI
  175. Purinton, 2021, Beyond vertical point accuracy: assessing inter-pixel consistency in 30 m global DEMs for the Arid Central Andes, Front. Earth Sci., 9, 10.3389/feart.2021.758606 · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Okolie, C. J., & Smit, J. L.
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2022-06
Volume
188
Pages
1-29
ISSN
0924-2716
Cited by
139
References
3
Language
en
Funders
University of Cape Town

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review