The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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Observations were analysed to identify and discard outliers: 
PAN: 8 outliers (4 groups - 12 measurements), 
RGB: 5 outliers (3 groups - 9 measurements) 
After outliers discarding standard deviations received 0.20 
(PAN) and 0.14 (RGB). Repeatability, reproducibility and RMS 
for the test areas are for PAN in table (Table 1) presented and in 
table (Table 2) for RGB. Comparison between: bias (average 
observed area minus reference area), standard deviation (from 
the average observed area), reproducibility and RMS is possible 
in tables (Table 3, Table 4). Relationships between 
reproducibility, RRMS and the test area are shown on the 
diagrams (Figure 5 and Figure 6). Mean value of reproducibility 
for PAN is equal: 13.3% and respectively RRMS: 21.6%. Better 
results were obtained for RGB, mean reproducibility is equal: 
8.9% and respectively RRMS: 12.7%. Photointerpretation of 
PAN was biased more than RGB (see Table 3 and Table 4). 
Mean bias on PAN for test areas 1,3,4,6 (digitized impervious 
surface) was -18% and for test areas 2, 5 (digitized pervious 
surface): +12.3% (average error of Ain Table 3 is 16%). It 
means that in photointerpretation on PAN impervious surfaces 
were underestimated in compare to the reference. This 
relationships is not observed on the RGB, bias is smaller than 
on PAN, varies from minus to plus values and mean of RRMS 
is 2.9% (Table 4). Relationships between the RRM and test area 
are presented for PAN and RGB on the diagrams, respectively 
on Figure 5 and Figure 6. The bias, appearing in the PAN 
observations, is possible to be seen on the figure Figure 5 as a 
shift of RRMS up to the reproducibility (ISO). This 
phenomenon is not, in this scale, observed in RGB observations 
(compare Figure 5 and Figure 6). For example, RRMS for the 
test area 3 and 4 is almost equal to the reproducibility, and the 
bias is small. 
Before statistical analysis, values of RMS for all test area and 
all pixels, were combined into 10 groups depending of the mean 
imperviousness factor from 0 to 100% by each 10%. Bias, and 
RMS of imperviousness factor are presented in 10 groups for 
PAN (Figure 8) and RGB (Figure 9) observations. Maximum 
bias in PAN measurements was slightly more then -30%, it 
means that operators underestimated impervious areas. 
Absolute RMS increases with increasing of imperviousness 
factor even more then 35% on IKONOS PAN. IKONOS 
PANSHARP (RGB) allowed obtaining much better results 
(Figure 9), small, neglected bias and RMS in some cases only 
slightly more then 10%. 
Finally, the RRMS of impervious surface area was calculated in 
each pixels for comparison to the RRMS of impervious surface 
area calculated for all test area (compare Figure 5, 6 and Figure 
10,11). In the analysis the value of the impervious area should 
be take into consideration, Fig, 5, 6: 10 000 - 60 000 sq m, and 
Figure 10, 11:0- 900 sq m. 
The figures: Figure 7 - Figure 11 show the accuracy analysis of 
data, in simulated Landsat pixels (30m). Relation between the 
RRMS, relative area error of impervious surface in the pixel, 
and reference area of impervious surface is presented on 
diagram (Figure 7). Usually, the absolute area error increases 
with increasing of the measured area, but simultaneously 
RRMS is decreasing. The relationship is valid for the area from 
digitalisation of remote sensing images or surveying. It is easy 
to be observed in the case of cadastre parcels. Our object of 
interest was however other. There are in fact many polygons, 
analysed totally in test areas (Figure 5 and Figure 6). Therefore, 
the tendency of decreasing of RRMS with the area is slightly to 
be noticed on the diagram (Figure 5) and better in the RGB 
observations (Figure 6). The total measured impervious areas in 
test areas are varying between 10 000 - 60 000 sq m, and the 
RRMS varies from 6%, in RGB, to even 25 % in PAN 
observations. 
Analysing the same relationship in grid of 30m, we could obtain 
even huge error for small areas heading to zero, because in this 
case RRMS heads to infinity. Maximum area in grid 30m is 
9000 sq m, so in Landsat pixel impervious area varies from 0 
to 900m, and RRMS varies in range of zero to infinity. But the 
decreasing tendency with area is noticeable on Figure 7 , even 
in the “cloud of points”. On the other hand we use results of 
photointerpretation as a test area in Landsat image classification. 
Therefore, we compared imperviousness factor calculated for 
pixel on the basis on all observations and on the reference. 
Usually we assume result of one photointerpretation as a 
reference. In our research we have 18 measurements because 6 
operators digitised the area 3 times. 
Figure 8 Relation between the absolute errors of 
imperviousness factor: RMS, bias and the imperviousness factor 
combined into 10 groups, PAN 
Figure 9 Relation between the absolute errors of 
imperviousness factor: RMS, bias and the imperviousness factor 
combined into 10 groups, RGB