In: Wagner W„ Sz&ely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B
Vertical Change Detection Result Comparison
| —»— Mask Based ~+~- Box-fitting Based Manual Extraction]
Figure 7. Vertical Change Detection Result Comparison
• For the annotation of the horizontal changes (areas of the
changed objects), the areas of the positive (resp. negative)
changed objects have been manually extracted using the
original 1KONOS panchromatic channel (resp. using the
LIDAR DSM data).
• For the annotation of the vertical changes (height of the
changed objects), we extract the height value in the
changed area from each DSM manually, and calculate the
difference.
The final change detection results are summarized in Table 1.
To compare the two automatic detection result and manual
extraction result, we show the vertical and horinzontal change
extraction result separately in Figures 7 and 8(For the
negative changes, we use the absolute values in Figure 7). As
the 4 lh and 5 th changes could not be separated in the detected
mask, we consider them as one changed object in the
comparison procedure. Also false alarms are omitted in our
comparison scheme. According to the error bars (get from the
standard deviation of the height value distribution in the
mask area), the detected vertical changes fit well with the
manual extraction result. For the 1 st building, the manual
extraction result shows relatively larger difference with both
the mask based and box-fitting based automatic extraction
result. In order to explain such behaviour, we compare the
generated DSMs to the IKONOS panchromatic image. As
showed in Figure 9, the IKONOS-DSM in this area has poor
quality, resulting in 3 big holes in the middle of the building,
and the building shape is strongly transformed. This explains
well the large height difference found between our change
maps and the manual extraction one.
Figure 9. 1 st changed building analysis: (a) LiDAR-DSM; (b)
IKONOS-DSM; (c) IKONOS-PAN-2005; (d) Mask with
IKONOS-DSM; (e) Masks with the IKONOS-PAN-2005
Horizontal Change Detection Result Comparison
Figure 8. Horizontal Change Detection Result Comparison
In the Figure 9 (d-e), the red mask represents the masked
shape, while the blue rectangle is the extracted change
building shape after box-fitting. We can see that the mask fits
well with the building area in IKONOS-DSM, but has quite
large difference with IKONOS-PAN-2005, after the box
fitting, the right parts of the outliers are successfully
recovered. But the result is limited by the rectangular shape
assumed in the box-fitting procedure, which displayed by the
false alarm pixels in the left upper part inside the blue colour
rectangle. Those situations conduct the relative lower vertical
changes, while higher horizontal changes in the extraction
results.
5. CONCLUSION
In this paper, a 3D change detection approach based on
DSMs is proposed and evaluated to detect changes that have
occurred in the city centre of Munich between 2003 and
2005.
The whole procedure is divided into 3 steps. First, we
generate and co-register DSMs acquired at two different
epochs. Then, we compute the “robust difference images” in
order to reduce the noise coming from the different nature of
the DSMs used. In fact, the random variations of the stereo
images acquisition conditions as well as the blunders caused
during the automatic matching and DEM generation process
makes urban structures look different from one DSM to
another, especially for building walls and edges. As our focus
in this paper is the monitoring of urban changes, noise
reduction is essential. After that, we generate the change map
with both vertical and horizontal change information. To
overcome the poor quality of the DSM, we refine the changed
buildings to rectangular shape. To confirm the validity of our
approach, we compare our results with manual extracted
ground truth figures.
It has been shown that DSMs generated from optical stereo
imagery could be reliable sources for efficient 3D change
detection. The extracted change maps demonstrate the ground
surface changes in most parts of the test areas. But when the
DSM does not meet the required quality and can not show the
real situation, the result will be influenced. In addition, only
rectangular building shapes have been considered in our
refinement procedure, the change results will certainly
improve when more building shapes are included.
ACKNOWLEDGEMENTS
The authors wish to thank Beril Sirmacek for contributing her
box-fitting algorithm, and Hossein Arefi for his advice in the
pre-processing of the DSM.