Since we have already thinned the objects to represent only 
those with relevant height changes, the per-pixel probability 
measure is no longer important in comparison to the object 
shape measures. So, we also have reduced the Pixel Probability 
Weight from the default 50 percent to only 1 percent. This way 
the final probabilities are mainly derived from the shape cues, 
not from the height differences. 
Vector Clean-up Operators 
After the polygon objects have been updated with geometry- 
based object cues, the final step is to filter out all low 
probability objects. Objects with low final probabilities are 
more likely to be trees than they are buildings because of the 
shape measures we applied. 
A final probability filter is applied at 0.5. 
1.4 Quality Assurance 
Running the IMAGINE Objective feature model identified 19 
objects representing new building construction between the two 
dates of LiDAR collection. To check the validity of these 
results, the following procedure was followed. 
Errors of Commission 
Using ERDAS IMAGINE 2011, two 2D views were open 
alongside each other. The “after” LiDAR data was loaded into 
the left view and the “before” into the right view. The 
IMAGINE Objective results were overlain into the left view and 
their symbology changed to an unfilled, outlined polygon. The 
two views were linked and scales equalized. 
We started the attribute table for the shapefile and selected all 
records. 
In the Table tab, the Zoom to Item controls were used to drive 
to each polygon one by one. By visually comparing the height 
information at the same location, it was easy to determine 
whether each polygon was correctly identified as new building 
construction or if it was a false positive. 
  
  
  
  
  
  
Figure 5. This polygon is a correct detection. 
  
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 
   
  
  
  
  
  
   
  
Figure 6. This polygon appears to be a false detection (and 
would be easier to confirm if colour or false-colour infrared 
imagery were available as a simultaneous or near-simultaneous 
capture). 
False detections can easily be deleted from the vector layer as 
they are reviewed. 
Errors of Omission 
To find new building construction missed by the IMAGINE 
Objective change detection routine, both LIDAR point clouds 
were loaded into a single 2D View with the results shapefile 
overlain. The Swipe (or Blend) tools were then used to peel 
away the “after” data and visually compare with the “before.” 
This enables the human eye to detect other locations of height 
change which might be buildings and which can be investigated 
more closely. 
Results 
Of the 19 detected objects, 5 appear to be false positive (errors 
of commission) resulting from locations of height change which 
are not actually new building construction. Only two false 
negatives (errors of omission) were identified in the area. 
1.5 Discussion 
Spectral Information 
Analysing the intermediary results of the IMAGINE Objective 
model (a capability which is a significant advantage of 
IMAGINE Objective), the two errors of omission are objects 
which came very close to meeting the probability cut-off for 
inclusion into the identified set. 
On the other hand, the errors of commission appear to generally 
represent specific trees (with a large height difference, whether 
for new planting, leaf on/off conditions, or other reasons) which 
resulted in objects with a high degree of similarity in shape to 
rectangular buildings due to either having high rectangularity 
measures or high orthogonality measures. 
If the analysis included a source of spectral information such as 
natural colour or preferably 3- or 4-band data with red and 
near-infrared wavelengths, it could discriminate between 
vegetation objects and buildings. In this manner, the majority of 
false positives would be rejected and it would be far more likely 
that the relative probability of the few false negatives would 
increase thereby including them into the correct detections 
without increasing the number of false positives unduly.