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Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.

Digital Terrain Model (DTM) of a specific area represents the ground surface elevations in that area. This is a very important surface model for modelling of the ground landscape in addition to the wide ranges of engineering and environmental applications that require a DTM. Also, DTM is crucial in planning of different types of infrastructure projects such as irrigation or sewerage systems. It is also, very important in the optimum design and building of road and transportation networks. These important applications and many others require the creation of an accurate DTM. Light Detection and Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects [

The majority of the approaches for extraction of DTMs from LiDAR measurements have been based on the concept of searching for the lowest points in a user-defined neighborhood by using morphological filters using curvature methods and identifying them as bare earth [

Baligh et al. 2008 [

Classification of ground and non-ground points is an important issue for many applications of airborne LiDAR measurements. Hui et al. 2019 [

Sharma et al. 2010, [

Rashidi and Rastiveis, 2018 [

Hu et al. 2017 [

This research aimed at exploration of the different approaches for filtering of Airborne LiDAR measurements for removal of non-ground objects in order to create a Digital Terrain Model (DTM) that can be employed in a wide range of engineering and environmental applications. In addition, it aimed at applying a comparative study of the application of three main filtering techniques namely; Gaussian low pass filter, focal analysis mean filter, and DTM slope-based filter on airborne LiDAR DSMs in urban residential landscape at varying filter window sizes for creation of a reliable DTM in addition to evaluation of the effects of the sizes of user-defined windows of Gaussian low pass filter, focal analysis mean filter, and DTM slope-based filter on the qualities the created DTMs.

A sample of LiDAR data of the ISPRS WG III/4 Test Project on Urban Classification and 3D Building Reconstruction has been provided by ISPRS WG III/4 and the German Association of Photogrammetry and Remote Sensing (DGPF) [^{2}. However, point density varies considerably over the whole block depending on the overlap, but in the areas covered by only one strip, the mean point density is about 4.0 points/m^{2} (Rottensteiner et al. 2013) [^{2} of average dimensions of about 500 meters in swath width by about 1450 meters as the swath length. The sample data consists of 3,774,279 LiDAR data measurements giving LiDAR point cloud density of 5.2059 points per one meter squared (pts/m^{2}). This means that one LiDAR measurement has been recorded for every 0.1921 square meters in average. The statistical analysis of the data set has shown a minimum elevation of 165.203 meters and a maximum elevation of 298.932 meters giving a range of elevations of 133.729 meters, see

A Digital Surface Model (DSM), see

Vaihingen_Strip_03.las LiDAR data file using SAGA 6.4 open source GIS software where the Inverse Distance Weighting (IDW) interpolation method with power of four and grid resolution of half a meter has been used as the interpolation parameters. The generated DSM in

Low pass filtering is a spatial filtering process based on using low pass filters designed to emphasize low spatial frequency features and deemphasize high spatial frequency features of an image in a spatial domain [

In this study Gaussian low pass filter has been performed on the LiDAR DSM,

Gaussian low pass filter window size | Airborne LiDAR DSM | DTM from filter 3 × 3 | DTM from filter 7 × 7 | DTM from filter 15 × 15 | DTM from filter 21 × 21 | DTM from filter 25 × 25 | DTM from filter 31 × 31 | DTM from filter 35 × 35 | DTM from filter 41 × 41 |
---|---|---|---|---|---|---|---|---|---|

Statistical quantity | |||||||||

Min. (m) | 165.259 | 165.858 | 182.825 | 223.183 | 236.151 | 242.958 | 245.415 | 245.517 | 245.644 |

Max. (m) | 292.764 | 291.445 | 289.265 | 289.079 | 287.991 | 286.884 | 285.822 | 284.944 | 283.984 |

Range (m) | 127.504 | 125.588 | 106.440 | 65.896 | 51.840 | 43.926 | 40.408 | 39.426 | 38.339 |

Mean (m) | 265.289 | 265.289 | 265.289 | 265.289 | 265.289 | 265.289 | 265.290 | 265.290 | 265.290 |

Median (m) | 265.381 | 265.393 | 265.455 | 265.550 | 265.606 | 265.654 | 265.689 | 265.710 | 265.746 |

Root Mean Square (m) | 265.425 | 265.425 | 265.423 | 265.419 | 265.417 | 265.415 | 265.413 | 265.412 | 265.411 |

Standard Deviation (m) | 8.487 | 8.468 | 8.406 | 8.297 | 8.223 | 8.156 | 8.097 | 8.062 | 8.015 |

Skewness (m) | −0.318 | −0.321 | −0.332 | −0.354 | −0.372 | −0.387 | −0.401 | −0.408 | −0.417 |

Kurtosis (m) | 2.595 | 2.585 | 2.524 | 2.464 | 2.444 | 2.428 | 2.414 | 2.405 | 2.394 |

Focal analysis mean filter has been acknowledged by some authors as a method for smoothing of the DSMs and attenuation of the high frequencies [

DTM slope-based filter is a grid filtering approach that works under the open source GIS software, SAGA (System for Automated Scientific Analysis) and can be used to filter a DSM to classify the grid cells into a bare earth layer and a removed object layer that also known as ground layer and non-ground layer respectively. The filtering approach is based on the concepts described by Vosselman, 2000 [

Focal mean filter window size | Airborne LiDAR DSM | DTM from filter 3 × 3 | DTM from filter 7 × 7 | DTM from filter 15 × 15 | DTM from filter 21 × 21 | DTM from filter 25 × 25 | DTM from filter 31 × 31 | DTM from filter 35 × 35 | DTM from filter 41 × 41 |
---|---|---|---|---|---|---|---|---|---|

Statistical quantity | |||||||||

Min. (m) | 165.259 | 168.355 | 200.914 | 239.984 | 245.254 | 245.435 | 245.553 | 245.636 | 245.750 |

Max. (m) | 292.764 | 290.246 | 289.328 | 288.654 | 286.975 | 286.516 | 284.712 | 283.665 | 282.209 |

Range (m) | 127.504 | 121.890 | 88.414 | 48.670 | 41.720 | 41.081 | 39.159 | 38.028 | 36.459 |

Mean (m) | 265.289 | 265.289 | 265.289 | 265.289 | 265.290 | 265.290 | 265.290 | 265.289 | 265.289 |

Median (m) | 265.381 | 265.417 | 265.495 | 265.605 | 265.667 | 265.698 | 265.744 | 265.768 | 265.801 |

Root Mean Square (m) | 265.425 | 265.424 | 265.421 | 265.417 | 265.414 | 265.413 | 265.411 | 265.410 | 265.408 |

Standard Deviation (m) | 8.487 | 8.441 | 8.368 | 8.228 | 8.140 | 8.089 | 8.023 | 7.986 | 7.937 |

Skewness (m) | −0.318 | −0.326 | −0.339 | −0.370 | −0.391 | −0.403 | −0.416 | −0.423 | −0.430 |

Kurtosis (m) | 2.595 | 2.561 | 2.493 | 2.445 | 2.425 | 2.413 | 2.397 | 2.388 | 2.377 |

Figures 15-20 depict bare earth models extracted from DTM slope-based filtering of airborne LiDAR DSM with window sizes of 3 × 3, 7 × 7, 15 × 15, 25 × 25, 35 × 35 and 41 × 41 respectively. As shown in the figures the DTM slope-based filter has removed non-ground object leaving bare earth models that can be DTMs of the test area but with clear gaps of no data at the positions of the removed non-ground objects. In

Slope based filter window size | Airborne LiDAR DSM | DTM from filter 3 × 3 | DTM from filter 7 × 7 | DTM from filter 15 × 15 | DTM from filter 21 × 21 | DTM from filter 25 × 25 | DTM from filter 31 × 31 | DTM from filter 35 × 35 | DTM from filter 41 × 41 |
---|---|---|---|---|---|---|---|---|---|

Statistical quantity | |||||||||

Min. (m) | 165.259 | 165.259 | 165.259 | 165.259 | 165.259 | 165.259 | 165.259 | 165.259 | 165.259 |

Max. (m) | 292.764 | 289.281 | 289.252 | 286.208 | 286.179 | 285.617 | 285.538 | 280.444 | 280.444 |

Range (m) | 127.504 | 124.022 | 123.993 | 120.949 | 120.920 | 120.358 | 120.279 | 115.185 | 115.185 |

Mean (m) | 265.289 | 263.873 | 263.467 | 262.912 | 262.677 | 262.590 | 262.530 | 262.502 | 262.470 |

Median (m) | 265.381 | 263.947 | 263.654 | 263.263 | 263.137 | 263.088 | 263.049 | 263.029 | 263.008 |

Root Mean Square (m) | 265.425 | 264.002 | 263.592 | 263.030 | 262.792 | 262.703 | 262.643 | 262.615 | 262.583 |

Standard Deviation (m) | 8.487 | 8.265 | 8.116 | 7.872 | 7.762 | 7.727 | 7.714 | 7.710 | 7.707 |

Skewness (m) | −0.318 | −0.267 | −0.269 | −0.295 | −0.313 | −0.315 | −0.307 | −0.301 | −0.293 |

Kurtosis (m) | 2.595 | 2.443 | 2.436 | 2.403 | 2.371 | 2.357 | 2.350 | 2.348 | 2.347 |

extraction from Gaussian low pass filter and focal analysis mean filter the minimum elevations of the created bare earth models have kept unchanged with all the applied window sizes of the DTM slope-based filter. This means that the DTM slope-based filter keeps the minimum elevation of the original DSM unchanged. On the other hand, the maximum elevations, ranges of elevations, means of elevations, medians of elevations, root mean squares of elevations, the standard deviations of elevations and kurtosis of the created bare earth models decreased with increasing the window sizes of the DTM slope-based filter. However, the skewness of the created bare earth models has increased with increasing the window size of the DTM slope-based filter.

As shown in Figures 15-20 the created bare earth models from the application of the DTM slope-based filter of varying window sizes are full of no data gaps at the positions of the removed objects that affect their exploitation as DTMs in various applications. So, these bare earth models need to be subjected to an interpolation technique to fill the gaps and obtain a continuous surface representing the earth’s surface as a DTM. In this study, the created bare earth models, Figures 15-20 have been subjected the tool “close the gaps with spline” under SAGA-GIS software package to fill all gaps in the bare earth models and obtain continuous DTMs as depicted in Figures 21-26. In

Better removal of non-ground objects can be observed in

Figures 27-32 depict charts that show comparative analysis of the DTMs produced from the three filters; the Gaussian low pass filter, the focal analysis mean filter and the DTM slope-based filter. In

the window sizes of the different filtering approaches. Such decreases in the maximum elevations of the produced DTMs from Gaussian low pass filter and those from the focal analysis mean filter due to increases in the window sizes of the filters have been almost steady while the corresponding decreases in the maximum elevation have been irregular in the case of the bare earth models produced from the DTM slope-based filter. The composite behaviors of the minimum elevations and the maximum elevations have been reflected in

Three filtering approaches for stripping off above ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter at varying window sizes have been applied on airborne LiDAR DSM for creation of a reliable DTM since a DTM can be involved a wide range of environmental and engineering applications. A dataset of airborne LiDAR data of the ISPRS WG III/4 Test Project on Urban Classification and 3D Building Reconstruction that was captured over Vaihingen in Germany over a pure residential area with small detached houses on 21 August 2008 by Leica Geosystems has been used in the study. Visual analysis has indicated that Gaussian low pass filter with varying window sizes has produced blurred DTMs which has been indications of attenuation of high frequencies that refer to non-ground objects with emphasizing of low frequencies pointing to the ground surface points. With increasing the window size of the Gaussian low pass filter much blurrier DTMs have been obtained referring to better attenuation of high frequencies and consequently better removal of non-ground objects. This has been clarified by the outcomes of the statistical analysis of the DTMs from the Gaussian low pass filters which has referred to decreases in the minimum elevations, maximum elevation , ranges of elevations , standard deviations of the DTMs in addition to skewness and kurtosis of DTMs, due to increasing the window sizes of the Gaussian low pass filter resulting in attenuation of increasing amounts of the high frequencies and emphasizing of the DSM low frequencies. On the other hand, visual analysis of the DTMs created from the application of the focal analysis mean filter has shown better removal of the above ground objects compared to that from Gaussian low pass filter especially at large window sizes. Thus, details of the non-ground objects have been almost diminished in the DTMs produced from the focal analysis filter of window sizes of 25 × 25, 35 × 35 and 41 × 41 although the statistical analysis results of the DTMs from focal analysis mean filter resemble to great extent those of the DTMs from Gaussian low pass filter of varying window sizes. Visual analysis of the bare earth models extracted through the use of the DTM slope-based filter has shown very different results compared to those from the other two filters. The bare earth models obtained from the application of the DTM slope-based filter have been full of gabs of no data values at the positions of the removed non-ground objects. The sizes and numbers of the no-data gabs have increased with increasing the window sizes of the DTM slope-based filter. With the application of the tool; “close the gap with spline” working under SAGA GIS software clear views pointing to bare earth models that can be DTMs have been obtained referring to efficient removal of non-ground objects from the LiDAR DSM especially at large window sizes of the filter.

Comparative analysis of the three filters together has shown that the minimum elevation of the DTMs from Gaussian low pass filter and those from the focal analysis mean filter have increased with increasing the widow sizes of the filter till window sizes of 21 × 21 and 31 × 31 in the case of Gaussian low pass filter and focal analysis mean filter respectively. On the other hand, the DTM slope-based filtering approach has kept the minimum elevation of the original DSM unchanged. However, the three filtering approaches have produced DTMs of decreasing maximum elevations due to increases in the filter window sizes. Also, the standard deviations of the created DTMs have decreased with increasing the window sizes of the three filters however, the decreases have been continuous and steady in the cases of Gaussian low pass and the focal analysis mean filters. This has not been the case for the bare earth models created from DTM slope-based filter where decreases in the standard deviations have been at high rates till window sizes of 31 × 31 in the mean while the standard deviation has kept unchanged with more increases in the filter window sizes. Also, increases in the window size of the Gaussian low pass filter and the focal analysis mean filter have produced DTMs of decreasing skewness and more symmetrical Gaussian curves while increasing of the window size of the DTM slope-based filter has produced bare earth models of fluctuated skewness values. However, the DTMs produced from Gaussian low pass filter and from focal analysis mean filter in addition to the bare earth models obtained from the DTM slope-based filter have recorded decreases in the kurtosis due to increases in the window sizes of the filters. This means that application of these filters with increases in the window sizes has produced better removal of noise and omission of outliers giving more consistent DTMs and bare earth models. More investigation could be necessary to improve the efficiency of the three filtering approaches in removal of non-ground objects and creation of reliable DTMs.

The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [Cramer, 2010]: http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html.

Asal, F.F.F. (2019) Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes. Advances in Remote Sensing, 8, 51-75. https://doi.org/10.4236/ars.2019.82004