2.13.2 3 Knife-edge Deygout Method
2. Local correction for each measured value
2.16 File Formats
2.16.5 Coverage Prediction Export and Reports
2.16.5.1 Filtering Coverage Predictions at Export
Raster and vector coverage predictions can be filtered at export in order to exclude holes and islands. Predictions are filtered by setting the colour of a pixel to the dominant colour of the bounding box, i.e., surrounding pixels, using a dispersion factor:
.
Here, is the distance from the pixel to be coloured to each pixel within the bounding box and is the value at that pixel.
In other words, the pixel will be coloured by the most representative value within this bounding box.
In both cases, an XML file describing the prediction is also created in the corresponding
’<doc_name>\{<GUID>}’ folder.
Field Type Description
TX_NAME Text Name of the transmitter
FILE_NAME Text Name of the transmitter’s BIL result file
RESOLUTION Float Resolution of the calculation, same as ’xdim’ and ’ydim’ in the HDR file
AREA_XMIN Float Same as ’ulxmap’ in the HDR file
AREA_XMAX Float Same as ’ulxmap’ + ’xdim’ * ’ncols’ in the HDR file
AREA_YMIN Float Same as ’ulymap’ in the HDR file
AREA_YMAX Float Same as ’ulymap’ + ’ydim’ * ’nrows’ in the HDR file
NBITS Float Same as ’nbits’ in the HDR file
NBANDS Float Same as ’nbands’ in the HDR file
BYTE_ORDER Float Same as ’byteorder’ in the HDR file
BAND_ROW_BYTES Float Same as ’bandrowbytes’ in the HDR file
TOTAL_ROW_BYTES Float Same as ’totalrowbytes’ in the HDR file
SKIP_BYTES Float Same as ’skipbytes’ in the HDR file
DATA_TYPE Text Same as ’datatype’ in the HDR file
NO_DATA_VALUE Same as ’nodatavalue’ in the HDR file
D2 – X 2
exp
D X
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The user-defined filtering percentage gives the size of the bounding box: pixels in each direction. In other words, the bounding box is increased by one pixel every 10 % (since Y is defined as a percentage).
2.16.5.2 Smoothing Coverage Predictions at Export
Vector coverage predictions can be smoothed at export in orer to simplify its contours. Predictions are smoothed by reducing the number of points defining the contours of the polygons using a vertex reduction routine that successively reduces the number of closely clustered vertices (vertex reduction within tolerance of prior vertex cluster, Douglas-Peucher polyline simplification).
Two smoothing methods exist for defining the degree of coverage smoothing: smoothing by percentage and smoothing by the maximum number of points.
Smoothing by Percentage
The user-defined smoothing percentage gives the approximation tolerance: , where is the user-defined export resolution. Tolerance is the interval within which Atoll tries to reduce the number of points.
For example, for three successive points, A1, A2, and A3 as shown in Figure 2.29 on page 119, A2 will be deleted if within this tolerance (and A1 and A3 will be directly linked) and A2 will be conserved if outside this tolerance.
Figure 2.27: Bounding box for prediction filtering
Y Y 10
Figure 2.28: Smoothing Tolerance Definition
A2 outside the tolerance interval A2 inside the tolerance interval Figure 2.29: Smoothing by Percentage
Z 2
--- R2 Z 20
---
R
Smoothing by Number of Points
The second method consists in defining a maximum number of points to be deleted. This number of points helps the algorithm to determine the optimised tolerance (see "Smoothing by Percentage" on page 119) such that, with this obtained tolerance, the number of points to be deleted will be lower than this value.
Let’s consider the following example ( ). Starting from the maximum possible tolerance, the number of points to be filtered out are estimated (circled in red in the following example ( )). If this number is greater than the maximum number of points defined by the user, Atoll reduces the tolerance until reaching the requested maximum number of points or less ( ). The first the number of points respecting the constraint is obtained, smoothing is applied by deleting these points and linking the remaining closest points ( ).
2.16.5.3 Examples of Prediction Export Filtering and Smoothing
Figure 2.31 on page 121 shows the original signal level coverage prediction whose filtered and smoothed exported results are presented in Figure 2.32 on page 121.
1
2
3
4
Figure 2.30: Smoothing by Number of Points
1 2
3 4
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2.16.5.4 Coverage Prediction Reports Over Focus/Computation Zones
Statistics are calculated in coverage prediction reports over the focus zone or the computation zone, if no focus zone exists, or the covered area, if neither zone exists.
If the reference surface area for the statistics is based on a focus or computation zone, there may be minute inaccuracies in the calculated statistics because of the difference in the surface area calculation methods:
• The surface areas of the zones (polygons) are calculated by triangulation.
• The surface area of a coverage predictions is calculated by counting the number of covered pixels and multiplying this number with the area of one pixel, calculated from resolution of the coverage prediction.
At the border of the focus or computation zone, a pixel is considered inside the zone if its centre is inside. Otherwise, the pixel is considered outside the zone. This estimation may give rise to inaccuracies.
Figure 2.31: Bounding box for prediction filtering
Filtering Percentage: 0 % Smoothing Percentage: 0 %
Filtering Percentage: 0 % Smoothing Percentage: 100 %
Filtering Percentage: 100 % Smoothing Percentage: 0 %
Filtering Percentage: 100 % Smoothing Percentage: 100 % Figure 2.32: Exported prediction with filtering and smoothing