The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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The main reason is that these high-rise segments of study area 
are covered densely by vegetations so that no laser penetration 
is possible there. This causes them to be classified in both 
“High-rise” and “Slightly variable” regions, and consequently to 
be misclassified as “Building”. In order to eliminate these 
erroneous pixels from “Building” class, an area threshold equal 
to 70 square meters is defined and applied to the resulting image. 
The final results are shown in Fig. 6(b). As can be seen all the 
remaining polygons are parts of the roofs of buildings. 
Figure 6 - (a) detected building pixels -including high-rise, 
dense vegetation- (b) Building detection final results 
At the middle of some building roofs in Fig. 6(b) there are some 
pixels classified as non-building while they also should belong 
to the building class. This happens wherever a high-rise feature 
exists on the roof. In the case of the large building at the east of 
the scene, the height difference on the roof changes so abruptly 
that our system fails to detect the whole building. As a result, 
our system has detected two individual buildings there. 
4. ACCURACY ASSESSMENT 
The final output of our system is a raster image with pixels 
classified into two classes; “Building” and “Non-building” 
pixels. To assess the accuracy of the detected buildings, we 
have compared our results of building detection with the 
reference map provided by the data provider. Due to some 
unknown reasons one of the buildings of the scene is not 
defined in the reference map. So we excluded the corresponding 
detected pixels from our results. Fig. 7 shows our results and the 
reference map. 
Figure 7 - Building detection results versus Ground truth 
The comparison of the resulting image with the reference map is 
done by the calculation of the Confusion Matrix using the RSI 
ENVI4.2 software. Table (1) shows the computed confusion 
matrix. 
Confusion 
matrix 
Reference Map 
Our Results 
Building 
Non-Building 
Total 
Building 
82185 (TP) 
1380 (FP) 
83565 
Non-Building 
9596 (FN) 
133783 (TN) 
143379 
Total 
91781 
135163 
226944 
Table (1) The pixel-by-pixel comparison results for our system. 
In Table (1), TP (i.e. True Positive) shows the number of pixels 
which have a “Building” label in both datasets. Similarly TN 
(i.e. True negative) equals to the number of pixels having “Non 
building” labels in both compared datasets. The definition of FN 
and FP numbers is straightforward. 
The evaluated data in Table (1) are the results of a pixel-by 
pixel comparison between our results and the reference map. 
The two following objective metrics [Lee et. al 2003] are 
employed by some authors (Sohn and Dowman 2007) in order 
to provide a quantitative assessment of our building detection 
system. 
Completeness = 100 * (TP / TP + FN) 
Correctness = 100 * (TP / TP + FP) 
The evaluated amount of Completeness metric for our results 
equals to 89.5% which shows the building detection percentage 
[Sohn and Dowman 2007]. And the amount of Correctness 
metric for our results equals to 98.3% which shows this 
percentage of the “Building” detected pixels belong to buildings 
indeed. 
The commission and omission errors are also evaluated in 
percentages and listed in Table (2). Errors of commission 
represent pixels that belong to another class that are labeled as 
belonging to the class of interest. Errors of omission represent 
pixels that belong to the ground truth class but the classification 
technique has failed to classify them into the proper class. 
For instance, the amount of 1.65 for Commission error of 
“Building” class states that 1.65% of pixels in “Building” class 
do not really belong to building roofs. The Omission error of 
“Building” class in Table (2) shows that 10.46% of building- 
roof pixels have been misclassified by our system as “Non 
building”. It’s obvious that Commission and Omission errors 
for “Building” class are complementary amounts to the 
aforementioned Correctness and Completeness metrics. 
Class 
Error 
Commission 
Omission 
Building 
1.65 
10.46 
Non-building 
6.69 
1.02 
Table (2) The evaluated Commission and Omission errors for 
our results in percentages 
The Overall accuracy is another metric which evaluates the 
accuracy of any classification process. This metric can be 
evaluated using the following formula: