International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
Finally, sub image 6-e shows changes due to different
shadowing effects caused by newly erected high-rise buildings
in the downtown area.
*
+
: e
|
”~
"
27
(c) - 1999
@-195%6 — | . (d)-1999
Figure 6. Change detection image (a), white pixels represent
changes. Sub-figures b, c, d, and e have been cropped
and closely examined
6. CONCLUSION AND RECOMMENDATIONS
This paper presents a new methodology for image registration
together with a suggested procedure for detecting changes
between the involved images. The developed approach has been
tested on real datasets, which showed its effectiveness in
registering and detecting changes among multi-source, multi-
resolution, and multi-temporal imagery.
The use of the MIHT procedure, for automatic registration of
multi-source imagery with varying geometric and radiometric
properties, has been explained. The presented approach used
linear features (straight-line segments) as the registration
primitives since they can be reliably extracted from the images.
The MIHT sequentially solves for the parameters involved in
the registration transformation function while establishing the
correspondence between conjugate primitives. Experimental
results using real data proved the feasibility and the robustness
of the MIHT strategy even when there was no complete
correspondence between conjugate lines in the images. This
robustness is attributed to the fact that the parameters are
estimated using common features in both datasets while non-
corresponding entities are filtered out prior to the parameter
estimation.
To avoid the effect of possible radiometric differences between
the registered images, due to different atmospheric conditions,
noise, and/or different spectral properties, the change detection
is based on derived edge images. The use of edge images is
attractive since it would lead to an effective detection of
urbanization activities as they are represented by a dense
distribution of edge cells. Also, a majority filter is applied to
compensate for small registration errors as well as eliminate
small gaps and isolated edges. The images are then subtracted
to produce a change image, which could be enhanced by
applying a majority filter to remove small regions. The change
detection results were found to be consistent with those visually
identified.
Future research will concentrate on automatic extraction of the
registration primitives, straight-line segments, from the input
imagery. Moreover, the origin of the detected changes will be
investigated (e.g., new residential community, new roads, etc.).
7. REFERENCES
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Canny, J., 1986. A computational approach to edge detection,
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source imagery, Photogrammetric Record, 19(105):pp. 5-21.
Habib, A., M. Morgan, and Y. Lee, 2001a. Integrating data
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