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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
6. CHANGE DETECTION AND MAP REVISION 
All of the results of the object extraction methods converted to 
vector layers. Images and aerial photographs together with 
extracted vector layers and old maps imported to a geodatabase 
in ArcGIS. Images saved in a raster catalog while vector layers 
saved in feature datasets. Both raster catalogs and feature 
datasets assigned the same coordinate system. All of the images 
and vector layers added in an ArcMap project to track changes. 
Two methods of change detection were used, comparison of the 
old and new extracted maps and comparison of the old maps 
with the recent images and aerial photographs. Images overlaid 
with aerial photographs and old digital maps. We examined 
those parts of the image where changes relative to the old maps 
had been occurred. Several kinds of changes were evident, some 
existing buildings had been vanished and some new ones had 
been constructed. In the city marginal areas, agricultural lands 
had been withdrawn in favor of man-made constructions. All of 
these changes manually digitized and saved in a dataset (named 
“Changes”) in the project geodatabase. In the second part of 
change detection process, the old maps overlaid with the new 
extracted maps. In this stage, different GIS methods were used 
to detect areas where changes had been occurred. Changed areas 
in the new maps were selected and exported as new layers and 
saved in the geodatabase (in the “Changes” dataset). At last, 
change layers merged together and converted to one layer. With 
the previous mentioned method, the change layer overlaid on 
the image to find those parts where editions were necessary. 
The final changes layer then overlaid on the old maps layer. 
Changed parts of the old maps and vanished features detected 
and newly constructed features added in an edition procedure. A 
new dataset called “UpdatedJLayers” created and each of the 
revised layers of the old maps imported to it. Labels and 
attributes of the newly added features inserted to each layer’s 
attribute table. All of the revised layers then added to map 
sessions according to the old maps indices to create new 
updated maps of the project. 
7. CONCLUSION 
In this study, we utilized different image fusion and object 
extraction methods to derive maximum information from high 
resolution satellite images and explained the process of using 
the extracted information for map revision. Comparison of the 
image fusion methods discussed in this study, multiplicative and 
wavelet methods were better in keeping spectral information, 
hence were good in terms of preserving color properties of the 
objects. IHS and PCA methods were better in keeping spatial 
details of the objects; however, IHS method was good enough 
in keeping spectral information as well. In sum, we concluded 
that IHS method is better owing to its fairly keeping both 
spectral and spatial information. Ikonos and QB MS images and 
the pan and pan-sharpened product of the Ikonos were used to 
extract different object classes. Multispectral images mainly 
employed in unsupervised and supervised classification while 
pan-sharpened product was a major help to extract smaller 
objects. Pan-sharpened image produced by PCA and IHS 
methods were very useful for detection of the boundaries of the 
objects in visual interpretation method. Pan-sharpened image 
produced by multiplicative and wavelet fusion methods utilized 
in automatic classification procedures due to their better 
performance in keeping spectral information. Results from 
automatic clustering methods and fuzzy classification needed 
more manual editing and visual inspection after vectorziation, 
even though they cost less time comparing to visual 
interpretation. Vectors extracted from classification methods 
were used for the revision procedure only after visual inspection 
and edition. 
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