Analysis and Validation of Grid Dem Generation Based on Gaussian Markov Random Field

Abstract

ABSTRACT:Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m 2 ) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

UAV-Based Digital Terrain Model Generation under Leaf-Off Conditions to Support Teak Plantations Inventories in Tropical Dry Forests. A Case of the Coastal Region of Ecuador

“…Despite this fact, the GMRF interpolation algorithm was able to properly fill the ground blanks yet producing a smooth and apparently truthful DTM. Furthermore, GMRF does not require to specify the local support or kernel (searching radius or maximum number of neighbors intervening in the interpolation of each grid point), which can be qualified as very advantageous, above all when dealing with low density ground points areas such as forest environments [39].…”

“…Finally, the scattered UAV-DAP derived ground points, potentially free of outliers, were interpolated to build a 20 cm grid spacing DTM comprising a square area of 36 m side by using the Gaussian Markov Random Field (GMRF) algorithm [39] (freely available code at ).…”

Effect of d-Dimensional Re-orderings on Lossless Compression of Radio-Astronomy and Digital Elevation Data

“…Existing literature indicates that DEM data can be simulated using IGRFs [61], [62], making the SRTM dataset a good realworld candidate for the model chosen by Memon, Neuhoff, and Shende [44]. Empirical evidence for the SRTM data following this model is given in Section II-B2.…”

New high-fidelity terrain modeling method constrained by terrain semanteme

“…The grid DEM is the most common data-based model [ 8 ], being composed of square grids typically generated through a traditional process, where in a triangulated irregular network (TIN) is constructed based on sampled points and interpolated to yield grid data. Studies to improve grid DEM accuracy generally consider the interpolation algorithm [ 9 – 13 ] and new data structures [ 14 , 15 ], which can partly mitigate native defects present in the grid structure.…”

“…Therefore, to improve DEM accuracy, many researchers have worked to improve the interpolation function. Examples of the resultant improvements include a new interpolation technique based on Coons patches [ 9 , 16 , 17 ], a new DEM generation method based on map algebra [ 10 – 12 ], a method based on the Gaussian Markov random field [ 13 ], and high-accuracy surface modeling [ 18 – 23 ], which has been proposed as a method of obtaining regular grid elevation data. In recent years, new data-sampling technology has been developed and new data processing methods have been proposed.…”