; even 
‘as an 
  
eas of 
larger 
been 
ce the 
) just 
strong 
edges 
S tree 
onting 
inges, 
af-off 
using 
vhich, 
o not 
valid 
pped. 
work 
for a 
leters. 
ly the 
new 
ically 
ish to 
(false 
scalar 
imum 
neters 
n the 
eight 
ations 
ilding 
Absolute Height Differences 
As mentioned above, the primary objective is to identify new 
building construction. For this, we are really only interested in 
the height difference values which are greater than zero. 
Consequently, the right-hand branch at the bottom of the model 
takes only the positive height difference values. 
The branch on the left takes the values less than zero and 
calculates their absolute value. This is done so that when 
looking for the probability of buildings being demolished we 
still have positive values (ie. a positive value of height 
decrease). 
Stretch 
So far, the values being calculated represent the actual 
difference in height. For the next stage of the analysis, we are 
only interested in the probability of change, so the greater the 
height difference, the higher the probability that change has 
truly occurred. The final calculation stretches the differences in 
heights so that they range from 0 to 1. 
Probability of Height Difference 
The two output raster objects represent the probability of height 
decrease (lower left) and the probability of height increase 
(lower right). 
1.3 Object-based Image Analysis using IMAGINE 
Objective 
IMAGINE Objective provides tools for feature extraction and 
update and change detection, enabling geospatial data layers to 
be created and maintained using remotely sensed imagery. 
IMAGINE Objective combines inferential learning with expert 
knowledge in a true object-oriented feature extraction 
environment. 
Segmentation 
The 16-bit “after” point cloud will be segmented in the Raster 
Object Creator node to create objects. The Raster Object 
Creator has been set as the start point of the analysis thereby 
skipping the Raster Pixel Process (RPP). Normally, a per-object 
probability would initially be calculated by the RPP. However, 
here we already have our “probability” values from the 
graphical model. 
So, instead of running a RPP, we have selected the 
Segmentation node, changed to the I/O (Input / Output) tab and 
specified the input raster layer as being the Probability of 
Height Increase layer. This is not the layer which will be 
segmented (the 16-bit “after” point cloud will be segmented to 
produce spatial objects) — this layer is instead used to determine 
the initial probability value that will be associated with each 
object. 
Remember that the “after” point cloud was stretched to a full 
16-bit data range. So, the properties for the segmentation 
process need to be correspondingly large / loose. The Minimum 
Value Difference was set to 100 and, in the Advanced Settings, 
the Edge Detection Threshold was set to 50. 
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 4. Segmentation results. Note that each segment is 
attributed with a pixel probability derived from the Probability 
of Height Increase layer. 
Raster-Object Operators 
An initial Probability Filter is applied to the segments to 
remove any segment with a probability of representing 
significant height increase less than 0.1: 
A Clump Size filter is then applied to remove any groups of 
segments which are smaller than 50 square meters, the smallest 
building that would be of interest: 
Remaining segments are then reclumped if contiguous. 
Raster to Vector Conversion 
The IMAGINE Objective software then converts the raster 
clumps to vector polygons so that they can be further analysed 
using object-based cue metrics such as area: 
Vector-Object Operators 
To assist in analysing the shapes of the remaining objects they 
are first processed using an outlier clipper (set to a mild 0.1) to 
remove any erroneous spikes: 
Polygons are then smoothed to remove the stair-step effects of 
converting pixel-based clumps to vector polygons using a mild 
0.1 smoothing factor: 
Finally, Generalize is applied at 0.4 meter thinning tolerance to 
ameliorate the stair-stepping effects: 
Vector-Object Processor 
At this stage we have numerous polygons remaining which 
represent objects with a high degree of a positive height change. 
Since we are interested in identifying new building construction 
these objects will then be measured for characteristics which 
would be found in buildings and which will therefore help 
distinguish new buildings from other types of height difference. 
The characteristics used include orthogonality and 
rectangularity. We could also use other cues, such as area, since 
buildings tend to have constrained area footprints. However, it 
is best to use only orthogonality and rectangularity because the 
valid ranges for the cue do not need to be specified by the user.