The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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measurements and singular measurement, instead of mean, the 
true value should be used, if it is accessible. Alternative method, 
to RMS, could be also involved to the accuracy assessment, 
especially if the experiment is properly prepared and 
measurements are repeated by a few operators. In this case, the 
experiment is similar to laboratory measurements usually made 
in chemical research, precisely determined by ISO standard. 
According ISO 5725 “Accuracy (trueness and precision) of 
measurement methods and results”, among others, the following 
notation is used: sample, operator, laboratory, equipment, 
repeatability and reproducibility. As a sample, in our research 
we understand digitised impervious surface on IKONOS, on 
each of test area. In the experiment, 6 operators have been 
taking part using 2 equipments (IKONOS PAN and RGB). Each 
operator is understood as a laboratory, so we have 6 
laboratories. Each test area has, as a reference, the impervious 
surface area, digitised on the aerial ortophoto. Six operators 
digitised PAN/RGB 3 times, so we have 18 measurements for 
each test area. Finally, 108 measurements were analysed 
because of 6 test area and 18 measurements of one test area. At 
the beginning, outliers were found for PAN and RGB images. 
According ISO 5725 outliers are found using graphical or 
numeric method, Corchan/Grubbs tests. In our research group, 
there was no statistician so we identified outliers in traditional 
way, as used usually in surveying. Relative differences between 
reference area and each measured area were calculated and 
histogram of the error was prepared for PAN and RGB (Figure 
4). Outliers were defined as 5% of the external measurements 
on the histogram. If at least one of measurement is marked as 
an outlier, all group of measurements (3 repetitions) are 
discarded. After outliers removing, accuracy analysis was 
performed. 
Assuming “y”, as a result of measurement of the impervious 
surface area for each test area for each measurement, the 
following can be written: 
y = m + B+ e 
where: 
m- expected value (reference impervious area), 
B -bias in repeatability condition (difference between measured 
area and reference area), 
e - random error in repeatability condition. 
Variance of B describes between laboratories variance: 
var (B) = CT 2 group 
where: 
cfgroup - standard deviation (between laboratories). 
Variance in repeatability condition for one laboratory is defined 
as following: 
var (e) = a 2 i 
CTi - standard deviation within group (laboratory), calculated for 
the test area digitized 3 times by one operator. 
Each operator (laboratory) is described by standard deviation a\. 
For all operators average variance is calculated, called variance 
of repeatability: 
CT 2 re P et=var(e) = ct 2 , . 
Finally, accuracy is defined as standard deviation of 
reproducibility and it is the sum of the between groups variance 
and the within groups variance: 
_2 2 , 2 
° reprod 0 group ' M- repet 
where: 
Ogroup- standard deviation in group (one for one test area), 
CT rep et- average standard deviation of repetibility (average of six 
standard deviations for each operator). 
Results of photointerpretation were validated using ISO 
standard and in traditional way basing on RMS. Besides 
absolute RMS [sq m], relative area error (RRMS - Relative 
Root Mean Square = RMS/reference area with value: 0-100% 
or 0-1) was analysed. 
4.2 Part 2 - influence of the photointerpretation accuracy 
on the imperviousness factor calculated in simulated 
Landsat pixel 
Landsat classification (e.g. unmixing) requires training area, 
obtained from field surveying or from photointerpretation of 
VHR images (e.g. IKONOS). In our research, we ask the 
question: how does the photointerpretation accuracy influence 
on the percent of impervious surface in Landsat pixel? 
For each test area, grid of 30m cell size was simulated. Then the 
impervious surface area was calculated in the grid. The 
procedure was performed for all measurements (18 observations 
for each test area) and for the reference. In each simulated 
Landsat pixel, area of impervious surface and percent of 
impervious surface area in the pixel was calculated for each 
measurement and for the reference. The percentage of 
impervious surface area in the pixel is called: imperviousness 
factor. Reference area and measured area were analysed in 100 
pixels for the test area. Analysis was made in two aspects: 
comparison to the relative area error (RRMS) calculated 
generally for all test areas (data processing described in 4.1 Part 
1) and evaluation of the absolute and relative error of 
imperviousness factor. Firstly, RRMS of the impervious area 
was calculated for each pixel. Then the differences between 
percent of impervious cover of the pixel basing on observations 
and on reference were calculated. Then, absolute RMS of 
imperviousness factor was calculated for each pixel. Finally, 
RMMS of impervious surface area was calculated for all pixels. 
Accuracy analysis was performed on the Landsat pixel level in 
10 groups of imperviousness factor varies from: from 0% to 
100% by each 10%. 
Figure 3 Part of test area no. 1 with overlaid all digitised on 
Pansharp colour IKONOS area; each pixel is labelled by 
percent of impervious surface [0,1]. 
5. RESULTS 
Initially, all observations for PAN and RGB were statistical 
analysed. Histogram of relative area error for PAN and RGB is 
on the figure (Figure 4) presented. Mean of the difference 
between observed area and reference area (bias of the method)