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 
    
, Over, son des 
it rong ket 
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). 
  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
   
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).