Third, we combined the objects with the height difference data
so that each object reflects the average change in height for that
object. Then, we analysed the objects for significant change to
remove noise and map the construction activity between the two
dates.
The next sections outline how an analyst can conduct this
process using ERDAS IMAGINE.
1.2 Point Cloud Differencing
Difference
After LAS
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Probsbilirv: 0F Meight Drcrease Probability of Height Increase
Figure 2.The graphical model used to produce Probability of
Height increase and Probability of Height Decrease images.
Input Point Cloud Data
The first two objects at the top of the model represent the input
point cloud datasets. To change the dataset simply double-click
the relevant object and select your input data in the resulting
File Chooser dialog.
ERDAS IMAGINE can directly read the LAS file format for
LiDAR data as well as several other formats used for point
cloud information. Alternatively, you can first use one of the
import or surface interpolation routines (in the Terrain
Preparation dialog) to convert your source data. This is useful if
you want greater control over parameters such as the spacing of
resulting raster surfaces or the use of different LIDAR returns.
Difference Calculation
The first function object in the model, fed by the two input
point clouds, performs the primary differencing by subtracting
the "before" values from the "after" values to generate the
difference in height.
However, point clouds generally have areas of Null or NoData
in them, for example where the LIDAR failed to return values
or in areas outside of the collection footprint. These are often
represented by using a NoData value in the file itself, such as
-32767. So as not to bias the height differencing at these values
the differencing does not apply the calculation at these locations
(instead passing through the value of -32767).
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Difference Image
The model does create a file of the raw differencing results even
though they are not directly utilised, it is generated simply as an
aid for Quality Assurance if needed.
Figure 3: A portion of the difference image.
To the lower left you will see three orange rectangular areas of
height increase which are new buildings. There is also a larger
violet rectangular area showing a building that has been
demolished
While these are clear to the human eye, you can also see the
“noise” which generally confuses any attempts to map just
features of interest, such as new buildings. There are strong
linear effects caused by slight mismatch of building edges
between the two dates, there are clumps representing strong tree
growth (top right) as well as other scattered effects representing
bad points in the LIDAR data, minor vegetation height changes,
variations caused by vegetation sway, leaf-on / leaf-off
variability, etc. All these would result in “false positives” using
more traditional approaches — areas identified as change which,
while they may represent actual change in the data, do not
represent the types of change we wish to map.
Minimum and Maximum Height Difference Constraints
One way to reduce false positives is to define the range of valid
heights that would occur with the type of change being mapped.
For example, when looking at new building construction work
we can assume that the minimum valid height difference for a
new building or new floor on an existing building is 1.5 meters.
Height differences of more than 40 meters are more likely the
result of bad height returns, or represent significant new
buildings which probably do not need to be automatically
identified.
The more you can constrain the valid height range you wish to
automatically map, the fewer errors of commission (false
positives) you introduce into the final results.
Consequently, the graphical model includes two input scalar
objects on the left side representing the maximum and minimum
height differences that are allowed. These are set at 40 meters
and 1.5 meters, respectively, but can be easily changed.
These scalars feed into a further function which takes in the
difference image and sets any values outside the valid height
range (including NoData values) to zero (since those locations
represent areas of zero probability of being a valid new building
construction).