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.
