- freely available
Remote Sens. 2010, 2(3), 833-860; https://doi.org/10.3390/rs2030833
2. Technical Considerations for Ground Filter Development
2.1. Ground Characteristics Used for LiDAR Ground Filtering
- Ground Surface Steepness. Surface slope is generally lower between two neighboring bare ground points than between one bare ground and one non-ground point . Hence, many ground filters define a point with slope larger than the maximum ground slope as a non-ground point [2,38,42]. The slope threshold value that distinguishes ground from non-ground points will likely differ for each surface type. Relatively flat urban surfaces may have a low threshold value, such as 30°. Complex surfaces such as mountain terrain or high relief forest canopy surfaces will have steeper slopes and may require a higher threshold to accurately distinguish ground from non-ground.
- Ground Surface Elevation Difference. Because most bare-ground surfaces have few sharp changes in elevation, the elevation difference from a ground point to neighboring ground points is usually lower than the difference to neighboring non-ground points. Therefore, points having an elevation difference higher than a location specific threshold are probably non-ground points, such as shrubs, trees, or buildings [12,38,43,44].
- Ground Surface Homogeneity. Ground surfaces are relatively continuous and smooth. Trees and buildings are the main non-ground features that should be removed from the measurements. But trees are usually less smooth in texture than bare ground and buildings  and may be removed based on morphologic characteristics .
2.2. Difficult Ground Features for LiDAR Ground Filtering
- Shrubs, especially those below one meter
- Short walls along walkways
- Buildings with different size and shape
- Hill cut-off edges
- Complex mixed covering
- Areas combined with low and high-relief terrains
- Lack of reliable accuracy assessment
2.3. General Ground Filtering Procedures
- Error Filtering: Local LiDAR point outliers are often randomly distributed over a study area. Outliers may be caused by airplanes, birds, or the sensor itself. These points have unreasonably high or low elevation values and must be removed during preprocessing [1,24,45,46,47]. The simplest way to identify these outliers is to examine the frequency distribution of elevation values [1,21,24]. Less obvious random errors can be identified by comparing each point to a local elevation reference [1,24]. Often Delaunay Triangulation [1,24] is used to determine these less obvious outliers. Manual examination of the dataset is another viable option .
- Interpolation, Resampling, or Reorganizing: Creating a raster dataset from the LiDAR point cloud is a necessary procedure for certain ground filtering algorithms. When a dataset is comprised of multiple flight lines of irregularly spaced LiDAR points, searching for neighboring points is a computationally intensive process . Sometimes the search for neighboring points can be accelerated by creating a Triangulated Irregular Network (TIN) [1,21], but more often than not the point cloud is rasterized so that the dataset can be searched with simple kernels [1,49,50,51,52,53]. As mentioned previously, certain LiDAR filtering algorithms rely on raster-based search logic .
- Ground Filtering: Ground filtering is the process of separating ground and non-ground points or pixels [1,47]. A variety of methods are used, depending on the local environmental conditions. Directional scanning [1,2,24], morphology-based [46,61,62,63], interpolation-based [50,64,65,66,67], and segmentation-based [34,68,69,70] algorithms are fully discussed in the following sections and comparisons are made between them and the newly developed Multi-directional Ground Filtering (MGF) algorithm.
- Generate DEM: A DEM is generated through interpolation of the ground points identified in the previous step [71,72]. Examples of popular interpolation methods include Inverse Distance Weighting , AMLE , Kriging [73,74], and hybrid methods that combine linear and non-linear interpolation . Studies prove that complicated interpolation methods may not generate better results than simple ones .
2.4. Study Site Selection Considerations for Ground Filtering
|Cell size (m)||Area (km2)||Context||Data selection||Site Number|
|||First||Site 1: 0.13|
Site 2: 0.72
|-||-||Low and high relief||Low-relief urban + sites with complex covering with a maximum 44.3º slope||4|
|||First||-||1||Site 1: 1.6|
Site 2: 3
|Urban, coastal, and high-relief forest sites||Site 1: low relief |
Site 2: coastal barrier island
Site 3: high-relief forest
|||First||1||1||Site 1: 1.3|
Site 2: 1.8
|Site 1: urban |
Site 2: forest
|Site 1: low-relief urban|
Site 2: high-relief forest
|||First||5.6||-||-||Low-relief rural sites||Low-relief areas with vegetation||2|
|||First||-||-||-||Urban and forest||Site 1: flat, undulated terrain|
Site 2: undulated terrain
Site 3: rough terrain, bushes+buildings
|||First||-||-||-||Low-relief forest and railway||Forest areas with railways||2|
|||-||>10||-||Site 1: -|
Site 2: 0.01
|High- resolution LiDAR data||Site 1: two buildings and forest|
Site 2: roads, street lamps, an underpass, and a small vegetation area
|||-||0.16||-||-||Low-relief with trees and buildings||Site 1: urban with large buildings|
Site 2: low-relief, buildings and trees
|||Second||2-16||-||0.01||High-resolution LiDAR data||Site 1: roads, street lamps, and underpass, and a small vegetation area|
Site 2: forest site
|||First or last||0.77||-||0.65||Forest||Forest with trees and buildings||1|
|||Site 1: last|
Site 2: first+last
|Site 1: 0.23|
Site 2: 0.81
|-||Site 1: 50|
Site 2: 6.25
|Low-relief with different features||Low-relief with trees, buildings, and railways||2|
|||First + last||0.477||-||0.34||Low-relief urban and forest sites||Urban and forest sites||2|
|||first||-||1~2||-||Various surfaces from ISPRS||Urban and rural||15|
|||first||-||1~2||-||Various surfaces from ISPRS||Urban and rural||19|
- Slope and Elevation Difference: Most filters make decisions based on a set of location specific thresholds. For example, the thresholds for the slope and elevation difference in low-relief urban areas are usually much smaller than those in high-relief forest area. If possible, researchers should test filters in both low- and high-relief areas.
- Size of Objects: If the filters are sensitive to the size of objects, users can test the flexibility of filters using different object sizes such as large coastal boundaries and large or small buildings.
- Surface Covering: It may be relatively easier to separate ground from non-ground in relatively flat areas covered with ground, trees, and buildings. However, an image containing a mixture of brush, short walls, and bridges can be much more troublesome. The complexity and spatial arrangement of objects covering the ground is another factor to consider when testing ground filter performance.
- Density: It will likely be more difficult to identify ground points in an area covered by dense urban features, such as electric poles, flags and cars.
- Size of Study Area: There are no criteria or restrictions in selecting the size of study sites, but areas that are too big may computationally too expensive to obtain optimal results. Study sites that are too small may overestimate filtering performance and mislead potential users of the filters. Table 1 indicates that areas around 1.5 km2 for one-meter resolution images may be an adequately sized area.
- Number of Study Sites: Testing ground filtering algorithms in various conditions is critical for a fair evaluation. Most researchers listed in Table 1 selected at least two study sites with different characteristics for assessing performance. If developers test only one image, they should choose a relatively larger area to cover different terrain conditions.
2.5. Accuracy Assessment
|Example||Ground truth data||Accuracy assessment|
|||Selected homogenous ground and building polygons||Quantitative analysis|
|||-||Quantitative analysis; total points; trend eliminated points; eliminated points; type I and type II errors|
|||Random point samples||Quantitative analysis|
|||Random point samples||Quantitative error analysis|
Type I and type II errors
|[1,21,60]||Classified ground and non-ground data for accuracy assessment||Quantitative analysis|
Overall accuracy, Kappa co-efficient
Type I and type II errors
|||No sample||Quantitative approach based on the filter property that the classification results improve with the point density|
|||No sample||Visual comparison, profile|
|||No sample||Visual comparison|
|||No sample||Visual comparison|
|||No sample||Visual comparison, profile analysis|
|||No sample||Visual comparison|
|||No sample||Visual comparison|
|||No sample||Visual comparison|
|||No sample||Visual comparison|
3. Review of Ground Filtering Methods
3.1. Ground Filtering Algorithms
|||First||-||Raw||1-D and bi-directional labeling||No||along scan lines||Slope|
|||First||Resampling to raster- lowest elevation||Raw||Compare three methods: |
1.elevation threshold with expanding window
2.maximum local slope
3. Progressive morphology
|Method 1: yes|
Method 2: no
Method 3: yes
|Method 1: increased mesh size|
Method 2: circle
Method 3: increased windows
|Method 1: elevation|
Method 2: local slope
Method 3: slope, elevation, cell size
|||First||-||Interpolated||Progressive morphology||Yes||Increased windows||Cell size, slope, elevation|
|||First||Delaunay triangulation||Raw||Erosion morphology and elevation||No||Predefined circle||Elevation, slope|
|||First+last||Resampling to raster||Raw||Cluster||No||Voronoi Neighbor||Elevation|
|||First||-||Interpolated||Linear Prediction of stationary random function after trend removal||Yes||2-D window||Elevation trend|
|||First+last||-||Raw||A despike VDF algorithm comparing local curvatures of point measurements||Yes||Predefined window||Cell size, elevation|
|||Second||Resampling to raster-lowest||Raw||Active contour and active shape model||Yes||Predefined window||Slope, elevation, energy function|
|||-||Resampling to raster-lowest||Raw||Active shape model based on energy function||Yes||Predefined window||Slope, elevation, energy function|
|||First+last||-||Raw||Iterative robust interpolation||Yes||Predefined window||Elevation difference, weight assigned to points|
|||first||-||Raw||Iterative robust interpolation||Yes||Predefined window||Elevation difference, weight assigned to points|
|||-||-||Raw||A least-squares adjustment with robust estimation||Yes||Predefined window||Slope, elevation, curvature|
|||First+last||-||Raw||A filter based on contour and interpolation||Yes||Predefined window||elevation|
|Interpolated||Multi-sale Hermite Transform||Yes||Predefined window||Elevation, slope|
|Interpolated||Multi-directional scanning combined with the roving window technique||Yes||Predefined window||Slope, elevation, the nearest ground, the label of the previous pixel|
|Segmentation/Cluster||Segmentation based on smoothness constraint|||
|Segment-based terrain interpolation|||
|Morphology||Dual rank filter based on dilation and erosion|||
|A morphological filer based on geodesic dilation|||
|Progressive morphological filter|||
|Directional Scanning||Bidirectional labeling|||
|Hybrid multi-directional ground filtering||[24,57]|
|Contour||Active contour and active shape model|||
|Active shape model based on energy function|||
|TIN||Local curvatures of point measurements|||
|The adaptive TIN model||[40,59]|
|Interpolation||The iterative robust interpolation||[64,65,66,76]|
|The multiscale curvature algorithm based on TPS interpolation|||
|A facet model|||
Segmentation- and Cluster-based Filters
Directional Scanning Filters
3.2. Comparative Studies and the MGF Algorithm
3.3. Factors Affecting Accuracy of Ground Filtering or Terrain Modeling
Density and Data Reduction
Original Data Error Caused by Instrument, Sensor or Non-surface features
Use of First or Last Return for Ground Filtering
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