Fig. 3 Subsets of the Quickbird images of 2006 and 2008, 
respectively (280 x 350 pixels) 
  
Fig.4 Manually digitized change image 
5. RESULTS AND ACCURACY ASSESSMENT 
In the following section, we present the results of the standard 
algorithms, the new CEST method, and the achieved 
accuracies. For the accuracy assessment three classes were 
selected: 
- Class 0 = unchanged buildings/background (black) 
- Class 1 7 changed or destroyed buildings (gray) 
- . Class 2 - new buildings (white) 
The reference is the manual digitization of Fig. 4. Accuracy 
assessment for classes 1 and 2 is based on 404 randomly chosen 
digitized objects. Only for class 0 all 404 objects were used. If 
the majority of the pixels inside an object are assigned the 
correct class, the whole object is considered as correctly 
detected. Producers’ accuracy, users’ accuracy and the kappa 
coefficients are calculated for all scenarios. 
5.1 Image Difference and Image Ratio 
For image difference, it is possible to detect the three different 
classes (positive change, negative change and no change). It can 
be seen, however, that large areas of pseudo change are 
detected (Fig. 5 left). Due to brightness changes of the 
sediment, change is especially detected in the north of the 
image. Most of the new buildings which appear in the T2 image 
are detected. Buildings which are unchanged are often 
identified as destroyed or changed buildings. For image ratio, it 
is difficult to find a threshold between new and 
changed/destroyed buildings. Therefore most of the buildings 
are detected as new buildings (Fig. 5 right). As with image 
difference, buildings which are unchanged are often detected as 
destroyed or changed. This leads to the extremely low 
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 
    
producers accuracy of 8.2 % for class 1 (changed or destroyed 
buildings). The amount of detected pseudo change is relatively 
low in comparison to image difference. 
  
Fig. 5 Change detection by image difference (left) and image 
ratio (right) 
52 PCA 
The image processed with the PCA change detection procedure 
shows a lot of pseudo change, especially in the south and west 
of the image. Similar to the image ratio result, most of the 
buildings are detected as new buildings (Fig. 6 left). Also, 
nearly 45 % of the unchanged buildings are classified as 
changed/destroyed. 30 % of the destroyed or changed buildings, 
on the other hand, are classified as unchanged. 
5.3 Delta Cue 
The delta cue method produces a change image with relatively 
high producer accuracies for class 0 and 1 (Fig. 6 right). More 
than 60 % of the unchanged buildings, however, were detected 
as changed/destroyed. Additionally, a large amount of pseudo 
change appears in the image, especially in the northeast. 
  
Fig. 6 Change detection by PCA (left) and delta cue (right) 
5.4 Post Classification 
For the post classification analysis we used the isodata 
algorithm, because no appropriate training areas were available. 
This method produces the lowest accuracies. Again, pseudo 
change poses a big problem (Fig. 7 left).