The DNM gives a representation of height information of
non-terrain objects, including high vegetation, buildings and
other objects, relative to the bare Earth surface. The DNM
represents all these aboveground objects upon a flat reference
plane. Figure 5 shows a profile in the DNM along the same
diagonal line as shown in Figure 4.
DNM-Diagonal
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Profile Graph
Figure 5. Profile diagonal of DNM
3.3.2 Digitization of Building Footprints: Without the
bi-return range data, it is impossible to reliably separate the
vegetation and buildings in the DNM automatically. Therefore,
Those buildings within the IHS are digitized manually on the
DOQ. There are total 1,120 building footprints created, and 533
larger polygons representing residential areas. In residential
areas, one polygon covers several houses because the heights of
houses do not change rapidly. According to NIMA's
specification, from DNM, the highest point inside the building
footprint is chosen as the roof top. Figure 6 shows all digitized
buildings and residential areas exaggerated by 5 times.
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Figure 6. Digitized buildings and residential areas
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3.3.3 VHM Generation: The VHM is created by deleting all
the buildings and residential areas from the DNM. First, the
layers of buildings and residential areas are converted from
features in vector format to grids, and are then deleted from the
DNM. The VHM is composed of those grid cells with heights
larger than 0.3 m.
3.3.4 Recovery of Valuable High Points: Outliers are
measured sample points that have very high or very low values
relative to the values in a dataset. In the first step of DTM
generation, most of removed outliers are correct, such as points
reflected by birds, but some valuable points are actually false
alarms incorrectly removed. By comparing the photo image
with the output of first filtered data carefully, two important
towers are found: one is the FAA control tower and another is
the general tower. The recovery of these two towers is to get
No
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
Z-value from raw lidar data by using the elevation of the
highest point within a 100-m diameter circle centering in the
footprint of towers. Figure 7 shows the two recovered towers.
Figure 7. Recovered towers from filtered data
3.3.8 Geometric Correction: As the extent of OIS is much
larger than the one of airfield photo image, a topological paper
map is scanned and used as background for the full scene of
OIS. In order to overlap well, the scanned map needs geometric
correction, image to image registration. This means two images
of like geometry and of the same geographic area are
positioned coincident with respect to one another so that
corresponding elements of the same ground area appear in the
same place on the registered images (Chen and Lee, 1992).
Here, the digital photo image of Santa Barbara Airport is
assigned as reference map and its spatial coordinates was input
to the scanned map. Five ground control points (GCPs) are
being selected to register the scanned map to the rectified base
photo image. To obtain an optimum effect, all GCPs are located
at road intersections with distinct points, and their RMS error is
0.563 m, this means the two maps matched very well (see
Figure 8). The left part is the topographical map, and the right
is the aerial image. All major roads are exactly aligned. To get a
smoother image without stair-stepped effect and high spatial
accuracy, the bilinear interpolation is used to resample the
geo-referenced map.
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Figure 8. Geometricallv corrected image
3.4 Create the Airport Glide Path and OIS Surfaces
According to NIMA Airfield Initiative Document, airport glide
paths and seven OIS surfaces are created. Figures 9 & 10 shows
these OIS surfaces in 2-D and 3-D. respectively. To avoid
airport incursion, the features within the SVTW, including
roads, railroads, bridges, rivers and ponds, are also digitized.
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